Publications

Shlomo Berkovsky, Ivan Cantador,  Domonkos Tikk. Collaborative Recommendations: Algorithms, Practical Challenges And Applications, World Scientific Publising (2019).

Abstract: Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e-commerce, multimedia and Web content. Collaborative-based methods have been the focus of recommender systems research for more than two decades.The unique feature of the compendium is the technical details of collaborative recommenders. The book chapters include algorithm implementations, elaborate on practical issues faced when deploying these algorithms in large-scale systems, describe various optimizations and decisions made, and list parameters of the algorithms.This must-have title is a useful reference materials for researchers, IT professionals and those keen to incorporate recommendation technologies into their systems and services.

Balázs Hidasi and Alexandros Karatzoglou. Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM (2018).

Abstract: Abstract: RNNs have been shown to be excellent models for sequential data and in particular for data that is generated from users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The better performance of these losses over alternatives, along with further tricks and improvements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly.

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. Proceedings of the 11th ACM Conference on Recommender Systems, RecSys (2017).

Abstract: Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs.

Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. Parallel recurrent neural network architectures for feature-rich session-based recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, RecSys (2016).

Abstract: Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles – such as matrix factorization – perform very poorly in this setting, thus item-to-item recommendations are used most of the time. However the items typically have rich feature representations such as pictures and text descriptions that can be used to model the sessions. Here we investigate how these features can be exploited in Recurrent Neural Network based session models using deep learning. We show that obvious approaches do not leverage these data sources. We thus introduce a number of parallel RNN (p-RNN) architectures to model sessions based on the clicks and the features (images and text) of the clicked items. We also propose alternative training strategies for p-RNNs that suit them better than standard training. We show that p-RNN architectures with proper training have significant performance improvements over feature-less session models while all session-based models outperform the item-to-item type baseline.

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks. Proceedings of the 4th International Conference on Learning Representations, ICLR (2016). 

Abstract: We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.

Benjamin Kille, Fabian Abel, Balázs Hidasi, and Sahin Albayrak. Using interaction signals for job recommendations. SIREMTI 2015: Workshop on Situation Recognition by Mining Temporal Information, held in conjunction with the 7th International Conference on Mobile Computing, Applications and Services, MobiCASE (2015).

Abstract: Job recommender systems depend on accurate feedback to improve their suggestions. Implicit feedback arises in terms of clicks, bookmarks and replies. We present results from a member inquiry conducted on a large-scale job portal. We analyse correlations between ratings and implicit signals to detect situations where members liked their suggestions. Results show that replies and bookmarks reflect preferences much better than clicks.

Martha Larson, Domonkos Tikk, Roberto Turrin. Overview of ACM RecSys CrowdRec 2015 Workshop: Crowdsourcing and Human Computation for Recommender Systems. Proceedings of the 9th ACM Conference on Recommender Systems, RecSys (2015).

Abstract: CrowdRec 2015 provides the recommender system community with a forum at which to discuss crowdsourcing and human computation. Systems that explicitly collect information from human annotators to improve recommendations are becoming more widespread. At this year’s workshop, we highlight incentivization and the issue of avoiding bias. We take a special look at how recommender systems can influence collective behavior, and the contribution that the crowd can make to recommender system evaluation.

Balázs Hidasi and Domonkos Tikk. Speeding up ALS learning via approximate methods for context-aware recommendations. Knowledge and Information Systems, Springer London, (2015).

Abstract: Implicit feedback-based recommendation problems, typically set in real-world applications, recently have been receiving more attention in the research community. From the practical point of view, scalability of such methods is crucial. However, factorization-based algorithms efficient in explicit rating data applied directly to implicit data are computationally inefficient; therefore, different techniques are needed to adapt to implicit feedback. For alternating least squares (ALS) learning, several research contributions have proposed efficient adaptation techniques for implicit feedback. These algorithms scale linearly with the number of nonzero data points, but cubically in the number of features, which is a computational bottleneck that prevents the efficient usage of accurate high factor models. Also, map-reduce type big data techniques are not viable with ALS learning, because there is no known technique that solves the high communication overhead required for random access of the feature matrices. To overcome this drawback, here we present two generic approximate variants for fast ALS learning, using conjugate gradient (CG) and coordinate descent (CD). Both CG and CD can be coupled with all methods using ALS learning. We demonstrate the advantages of fast ALS variants on iTALS, a generic context-aware algorithm, which applies ALS learning for tensor factorization on implicit data. In the experiments, we compare the approximate techniques with the base ALS learning in terms of training time, scalability, recommendation accuracy, and convergence. We show that the proposed solutions offer a trade-off between recommendation accuracy and speed of training time; this makes it possible to apply ALS-based methods efficiently even for billions of data points.

István Pilászy. Neighbor methods vs. matrix factorization: case studies of real-life recommendations. LSRS 2015: Workshop on Large Scale Recommender Systems, held in conjunction with the 9th ACM Conference on Recommender Systems, RecSys (2015).

Abstract: With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.

Balázs Hidasi. Context-aware preference modeling with factorization. Proceedings of the 9th ACM Conference on Recommender Systems, RecSys (2015). [Paper/Presentation/Poster]

Abstract: This work focuses on solving the context-aware implicit feedback based recommendation task with factorization and is heavily influenced by the practical considerations. I propose context-aware factorization algorithms that can efficiently work on implicit data. I generalize these algorithms and propose the General Factorization Framework (GFF) in which experimentation with novel preference models is possible. This practically useful, yet neglected feature results in models that are more appropriate for context-aware recommendations than the ones used by the state-of-the-art. I also propose a way to speed up and enhance scalability of the training process, that makes it viable to use the more accurate high factor models with reasonable training times.

Bottyán Németh. Scaling up Recommendation Services in Many Dimensions. Proceedings of the 9th ACM Conference on Recommender Systems, industry session, RecSys (2015).

Abstract: Gravity R&D has been providing recommendation engines as SaaS solutions since 2009. The company has a strong research focus and recommendation quality has always been their primary differentiating factor. Widely used or open source recommendation algorithms are of little use to our technology team as a result of the superiority of our in-house developed, proprietary algorithms. This presentation focuses on the challenges and solutions while scaling up the services.

Balázs Hidasi and Domonkos Tikk. General factorization framework for context-aware recommendations, Data Mining and Knowledge Discovery (2015).

Abstract: Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to solve the problem, although most of them assume explicit feedback which strongly limits their real-world applicability. While these algorithms apply various loss functions and optimization strategies, the preference modeling under context is less explored due to the lack of tools allowing for easy experimentation with various models. As context dimensions are introduced beyond users and items, the space of possible preference models and the importance of proper modeling largely increases. In this paper we propose a general factorization framework (GFF), a single flexible algorithm that takes the preference model as an input and computes latent feature matrices for the input dimensions. GFF allows us to easily experiment with various linear models on any context-aware recommendation task, be it explicit or implicit feedback based. The scaling properties makes it usable under real life circumstances as well. We demonstrate the framework’s potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets. We show in our experiments—performed on five real life, implicit feedback datasets—that proper preference modelling significantly increases recommendation accuracy, and previously unused models outperform the traditional ones. Novel models in GFF also outperform state-of-the-art factorization algorithms. We also extend the method to be fully compliant to the Multidimensional Dataspace Model, one of the most extensive data models of context-enriched data. Extended GFF allows the seamless incorporation of information into the factorization framework beyond context, like item metadata, social networks, session information, etc. Preliminary experiments show great potential of this capability.

Balázs Hidasi and Domonkos Tikk. Approximate modeling of continuous context in factorization algorithms. CARR 2014: 4th Workshop on Context-awareness in Retrieval and Recommendation, held in conjunction with 36th European Conference on Information Retrieval, ECIR (2014). [Presentation].

Abstract: Factorization based algorithms – such as matrix or tensor factorization – are widely used in the field of recommender systems. These methods model the relations between the entities of two or more dimensions. The entity based approach is suitable for dimensions such as users, items and several context types, where the domain of the context is nominal. Continuous and ordinal context dimensions are usually discretized and their values are used as nominal entities. While this enables the usage of continuous context in factorization methods, still much information is lost during the process. In this paper we propose two approaches for better modeling of the continuous context dimensions. Fuzzy event modeling tackles the problem through the uncertainty of the value of the observation in the context dimension. Fuzzy context modeling, on the other hand, enables context-states to overlap, thus certain observations are influenced by multiple context-states. Throughout the paper seasonality is used as an example of continuous context. We incorporate the modeling concepts into the iTALS algorithm, without degrading its scalability. The effect of the two approaches on recommendation accuracy is measured on five implicit feedback databases.

Balázs Hidasi. Factorization models for context-aware recommendations. Infocommunications Journal, VI/4 (2014).

Abstract: The field of implicit feedback based recommender algorithms have gained increased interest in the last few years, driven by the need of many practical applications where no explicit feedback is available. The main difficulty of this recommendation task is the lack of information on the negative preferences of the users that may lead to inaccurate recommendations and scalability issues. In this paper, we adopt the use of contextawareness to improve the accuracy of implicit models—a model extension technique that was applied successfully for explicit algorithms. We present a modified version of the iTALS algorithm (coined iTALSx) that uses a different underlying factorization model. We explore the key differences between these approaches and conduct experiments on five data sets to experimentally determine the advantages of the underlying models. We show that iTALSx outperforms the other method on sparser data sets and is able to model complex user–item relations with fewer factors.

Alan Said, Domonkos Tikk, and Paolo Cremonesi. Benchmarking – A methodology for ensuring the relative quality of recommendation systems in software engineering. In Recommendation Systems in Software Engineering (2014).

Abstract: This chapter describes the concepts involved in the process of benchmarking of recommendation systems. Benchmarking of recommendation systems is used to ensure the quality of a research system or production system in comparison to other systems, whether algorithmically, infrastructurally, or according to any sought-after quality. Specifically, the chapter presents evaluation of recommendation systems according to recommendation accuracy, technical constraints, and business values in the context of a multi-dimensional benchmarking and evaluation model encompassing any number of qualities into a final comparable metric. The focus is put on quality measures related to recommendation accuracy, technical factors, and business values. The chapter first introduces concepts related to evaluation and benchmarking of recommendation systems, continues with an overview of the current state of the art, then presents the multi-dimensional approach in detail. The chapter concludes with a brief discussion of the introduced concepts and a summary.

Alan Said, Martha Larson, Domonkos Tikk, Paolo Cremonesi, Alexandros Karatzoglou, Frank Hopfgartner, Roberto Turrin, and Joost Geurts. User-item reciprocity in recommender systems: Incentivizing the crowd. ProS 2014: Workshop on UMAP Projects Synergy, held in conjunction with 22nd Conference on User Modelling, Adaptation and Personalization, UMAP (2014).

Abstract: Data consumption has changed significantly in the last 10 years. The digital revolution and the Internet has brought an abundance of information to users. Recommender systems are a popular means of finding content that is both relevant and personalized. However, today’s users require better recommender systems, able of producing continuous data feeds keeping up with their instantaneous and mobile needs. The CrowdRec project addresses this demand by providing context-aware, resource-combining, socially-informed, interactive and scalable recommendations. The key insight of CrowdRec is that, in order to achieve the dense, high-quality, timely information required for such systems, it is necessary to move from passive user data collection, to more active techniques fostering user engagement. For this purpose, CrowdRec activates the crowd, soliciting input and feedback from the wider community.

Balázs Hidasi and Domonkos Tikk. Context-aware item-to-item recommendation within the factorization framework, CARR 2013: 3rd Workshop on Context-awareness in Retrieval and Recommendation, held in conjunction with 6th ACM International Conference on Web Search and Data Mining, WSDM (2013). [Presentation].

Abstract: Item-to-item recommendation - when the most similar items sought to the actual item - is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of factorization models. By doing so, one may transfer the well-known accuracy of factorization models observed at the personalized recommendations to the item-to-item case. This paper introduces context-awareness to item similarities in the factorization framework. Two levels of context-aware similarities are defined and applied to two context-aware implicit feedback based factorization methods (iTALS and iTALSx). We investigate the advantages and drawbacks of the approaches on four real life implicit feedback data sets and we characterize the conditions for their application. The results suggest that it is worth using contextual information for item-to-item recommendations in the factorization framework, however, one should carefully select the appropriate method to achieve similar accuracy gain than in the case of the more general item-to-user recommendation scenario.

Bottyán Németh, Gábor Takács, István Pilászy and Domonkos Tikk. Visualization of movie features in collaborative filtering. Proceedings of the 12th IEEE Int. Conf. on Intelligent Software Methodologies, Tools and Techniques, SoMeT (2013).

Abstract: In this paper we will describe a modification of the matrix factorization (MF) algorithm which allows visualizing the user and item characteristics. When applying MF for collaborative filtering, we get a model that represents the attributes of users and items by feature vectors. Some elements of these vectors may have understandable meaning for humans but due to the lack of internal connections between the feature vectors, these are difficult to visualize. In this paper we give a detailed description of a MF method enabling better visualization of features by arranging them into a 2D map, where via the calculation of the feature values we try to position features with similar “meaning” close to each other. To achieve this first we define a neighborhood relation on features, then we modify the MF so that we introduce a new term in the error function which penalizes the difference between the neighbor features. We show that this modification slightly decreases the accuracy of the model but we get well visualized feature maps. On the feature maps meanings can be associated with regions, and so we can provide an interesting explanation for the user why he/she was recommended the movie. Such plausible explanations may result in that users will better understand how the system works, which can also increase customer loyalty towards the service provider.

Balázs Hidasi and Domonkos Tikk. Initializing Matrix Factorization Methods on Implicit Feedback Databases. Journal of Universal Computer Science, 19(12) (2013).

Abstract: The implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is a much harder task than the explicit feedback based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Recently, the implicit feedback problem is being received more attention, as application oriented research gets more attractive within the field. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training. We present a general initialization framework that preserves the similarity between entities (users/items) when creating the initial feature vectors, where similarity is defined using e.g. context or metadata information. We demonstrate how the proposed initialization framework can be coupled with MF algorithms. We experiment with various similarity functions, different context and metadata based similarity concepts. The evaluation is performed on two implicit variants of the MovieLens 10M dataset and four real life implicit databases. We show that the initialization significantly improves the performance of the MF algorithms by most ranking measures.

Alejandro Bellogín, Pablo Castells, Alan Said and Domonkos Tikk. Workshop on Reproducibility and Replication in Recommender Systems Evaluation – RepSys. Proceedings of the 7th ACM Conference on Recommender Systems, RecSys (2013).

Abstract: Experiment replication and reproduction are key requirements for empirical research methodology, and an important open issue in the field of Recommender Systems. When an experiment is repeated by a different researcher and exactly the same result is obtained, we can say the experiment has been replicated. When the results are not exactly the same but the conclusions are compatible with the prior ones, we have a reproduction of the experiment. Reproducibility and replication involve recommendation algorithm implementations, experimental protocols, and evaluation metrics. While the problem of reproducibility and replication has been recognized in the Recommender Systems community, the need for a clear solution remains largely unmet, which motivates the present workshop.

Martha Larson, Alan Said, Yue Shi, Paolo Cremonesi, Domonkos Tikk and Alexandros Karatzoglou. Activating the Crowd: exploiting user-item reciprocity for recommendation. CrowdRec 2013: Workshop on Crowdsourcing and Human Computation for Recommender Systems, held in conjunction with 7th ACM Conference on Recommender Systems RecSys (2013).

Abstract: Recommender systems have always faced the problem of sparse data. In the current era, however, with its demand for highly personalized, real-time, context-aware recommendation, the sparse data problem only threatens to grow worse. Crowdsourcing, specifically, outsourcing micro-requests for information to the crowd, opens new possibilities to fight the sparse data challenge. In this paper, we lay out a vision for recommender systems that, instead of consulting an external crowd, rely on their own user base to actively supply the rich information needed to improve recommendations. We propose that recommender systems should create and exploit reciprocity between users and items. Specifically, recommender systems should not only recommend items for users (who would like to watch or buy them), but also recommend users for items (that need additional information in order that they can be better recommended by the system). Reciprocal recommendations provide a gentle incentivization that can be deployed non-invasively, yet is powerful enough to promote a productive symbiosis between users and items. By exploiting reciprocity, recommender systems can “look inwards” and activate their own user base to contribute the information needed to improve recommendations for the entire user community.

Dávid Zibriczky, Zoltán Petres, Márton Waszlavik and Domonkos Tikk. EPG content recommendation in large scale: a case study on interactive TV platform. Machine Learning with Multimedia Data – Special session at the 12th IEEE International Conference on Machine Learning and Applications, ICMLA (2013). [Presentation].

Abstract: Recommender systems in TV applications mostly focusing on the recommendation of video-on-demand (VOD) content, though the major part of users’ content consumption is realized on linear channel programs, termed EPG content. In this case study we present how we tackled the EPG recommendation task, which exhibits several differences compared to the VOD scenario, including the lack of explicit user feedbacks, the magnitude of cold start problem, as well as data cleaning and feature selection necessary to be applied on raw consumption data. We provide both offline and online model validation. First we showcase the typical approach in machine learning by evaluating models against recall in an offline setting. Then, we investigate in depth the real-world results of the recommendation app using the pre-trained models, and analyze how personalized recommendation influence users watching behavior. The experimentation results are based on our recommender system deployed at a Canadian IPTV service provider using Microsoft Media room middleware.

Gábor Takács and Domonkos Tikk. Alternating least squares for personalized ranking. Proceedings of the 6th ACM conference on Recommender Systems, RecSys (2012).

Abstract: Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user-item preference relationship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the system has to infer user preferences from indirect information: presence or absence of events, such as a user has viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional prediction mean squared error. The naive minimization of a ranking objective function is typically expensive. This difficulty is usually overcome by a trade-off: sacrificing the accuracy to some extent for computational efficiency by sampling the objective function. In this paper, we present a computationally effective approach for the direct minimization of a ranking objective function, without sampling. We demonstrate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.

Alan Said, Domonkos Tikk, and Andreas Hotho. The challenge of recommender systems challenges. Proceedings of the 6th ACM Conference on Recommender Systems, RecSys (2012). [Presentation].

Abstract: Recommender System Challenges such as the Netflix Prize, KDD Cup, etc. have contributed vastly to the development and adoptability of recommender systems. Each year a number of challenges or contests are organized covering different aspects of recommendation. In this tutorial and panel, we present some of the factors involved in successfully organizing a challenge, whether for reasons purely related to research, industrial challenges, or to widen the scope of recommender systems applications.

Dávid Zibriczky, Balázs Hidasi, Zoltán Petres, and Domonkos Tikk. Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback. TVMMP 2012: International Workshop on TV and Multimedia Personalization, held in conjunction with 20th conference on User Modeling, Adaptation, and Personalization, UMAP (2012).

Abstract: Recommender systems in TV applications mostly focus on the recommendation of video-on-demand (VOD) content, although the major part of users’ content consumption is realized on linear channel programs (live or recorded), termed EPG programs. The accurate collaborative filtering algorithms suitable for VOD recommendation cannot be directly carried over for EPG program recommendation. First, EPG program recommendation features the cold start problem; a significant part of EPG programs are new in the system. Second, and more importantly, without explicit user feedbacks (ratings) the algorithms have to model user preference based on the noisy and less directly interpretable implicit user feedbacks. In this paper, we present several approaches that overcome these difficulties, by applying pre-filtering on noisy low-level data and taking into account channel preferences of users and program metadata if available to cope with the cold start. Using time-dependent tensor factorization approaches, the temporal preferences of users are also reflected in recommendation, that also hints on the person watching the TV. Experiments were performed on a dataset of SaskTel, a Canadian IPTV service provider using Microsoft Mediaroom middleware.

Alan Said, Domonkos Tikk, Klára Stumpf, Yue Shi, Martha Larson, and Paolo Cremonesi. Recommender Systems Evaluation: A 3D Benchmark. RUE 2012: Workshop on Recommendation Utility Evaluation: Beyond RMSE, held in conjunction with the 6th ACM conference on Recommender systems, RecSys (2012).

Abstract: Recommender systems add value to vast content resources by matching users with items of interest. In recent years, immense progress has been made in recommendation techniques. The evaluation of these has however not been matched and is threatening to impede the further development of recommender systems. In this paper we propose an approach that addresses this impasse by formulating a novel evaluation concept adopting aspects from recommender systems research and industry. Our model can express the quality of a recommender algorithm from three perspectives, the end consumer (user), the service provider and the vendor (business and technique for both). We review current benchmarking activities and point out their shortcomings, which are addressed by our model. We also explain how our 3D benchmarking framework would apply to a specific use case.

Balázs Hidasi and Domonkos Tikk. Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback, Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD (2012). [Presentation].

Abstract: Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient in the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few if any implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate diverse context information into the model while maintaining its computational efficiency. In particular, we present two such context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage patterns typical of certain groups of items, e.g. to automatically tell apart product types or categories that are typically purchased repetitively (collectibles, grocery goods) or once (household appliances). Experiments performed on three implicit datasets (two proprietary ones and an implicit variant of the Netflix dataset) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.

Domonkos Tikk. From a toolkit of recommendation algorithms into a real business: the Gravity R&D experience. Workshop of Recommender systems challenge 2012, held in conjunction with the 6th ACM Conference on Recommender Systems, RecSys (2012).

Balázs Hidasi and Domonkos Tikk. Enhancing matrix factorization through initialization for implicit feedback databases. CARR 2012: 2nd Workshop on Context-awareness in Retrieval and Recommendation, held in conjunction with the 17th ACM International Conference on Intelligent User Interfaces, IUI (2012).

Abstract: The implicit feedback based recommendation problem—when only the user history is available but there are no ratings—is a much harder task than the explicit feed- back based recommendation problem, due to the inherent uncertainty of the interpretation of such user feedbacks. Still, this practically important recommendation task received less attention and therefore there are only a few common implicit feedback based algorithms and benchmark datasets. This paper focuses on a common matrix factorization method for the implicit problem and investigates if recommendation performance can be improved by appropriate initialization of the feature vectors before training. We present a general initialization framework that preserves the similarity between entities (users/items) when creating the initial feature vectors, where similarity is defined using e.g. context or metadata information. We demonstrate how the proposed initialization framework can be coupled with MF algorithms. The efficiency of the initialization is evaluated using various context and metadata based similarity concepts on two implicit variants of the MovieLens 10M dataset and one real life implicit database. It is shown that performance gain can attain 10% improvement in recall@50 and in AUC@50.

Nikos Manouselis, Alan Said, Domonkos Tikk, Jannis Hermanns, Benjamin Kille, Hendrik Drachsler, Katrien Verbert, and Kris Jack. Recommender systems challenge 2012. Proceedings of the 6th ACM conference on Recommender systems, RecSys (2012).

Abstract: The Recommender System Challenge 2012 invited participants to work on two tracks with real-world datasets and to submit their contributions that would be related to specific problem contexts. First of all, it asked participants to develop new algorithms and to compare them to other algorithms in given settings; in addition, it asked participants to explore with new recommendation methods, services, as well as added-value services related to recommendation.

Gábor Takács, István Pilászy, and Domonkos Tikk. Applications of the conjugate gradient method for implicit feedback collaborative filtering. Proceedings of the 5th ACM Conference on Recommender Systems, RecSys (2011).

Abstract: Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e-commerce, multimedia and Web content. Collaborative-based methods have been the focus of recommender systems research for more than two decades.The unique feature of the compendium is the technical details of collaborative recommenders. The book chapters include algorithm implementations, elaborate on practical issues faced when deploying these algorithms in large-scale systems, describe various optimizations and decisions made, and list parameters of the algorithms.This must-have title is a useful reference materials for researchers, IT professionals and those keen to incorporate recommendation technologies into their systems and services.

István Pilászy, Dávid Zibriczky, and Domonkos Tikk. Fast ALS-based matrix factorization for explicit and implicit feedback datasets. Proceedings of the 4th ACM Conference on Recommender Systems, RecSys (2010).

Abstract: Alternating least squares (ALS) is a powerful matrix factorization (MF) algorithm for both explicit and implicit feedback based recommender systems. As shown in many articles, increasing the number of latent factors (denoted by K) boosts the prediction accuracy of MF based recommender systems, including ALS as well. The price of the better accuracy is paid by the increased running time: the running time of the original version of ALS is proportional to K3. Yet, the running time of model building can be important in recommendation systems; if the model cannot keep up with the changing item portfolio and/or user profile, the prediction accuracy can be degraded. In this paper we present novel and fast ALS variants both for the implicit and explicit feedback datasets, which offers better trade-off between running time and accuracy. Due to the significantly lower computational complexity of the algorithm – linear in terms of K – the model being generated under the same amount of time is more accurate, since the faster training enables to build model with more latent factors. We demonstrate the efficiency of our ALS variants on two datasets using two performance measures, RMSE and average relative position (ARP), and show that either a significantly more accurate model can be generated under the same amount of time or a model with similar prediction accuracy can be created faster; for explicit feedback the speed-up factor can be even 5-10.

István Pilászy and Domonkos Tikk. Recommending new movies: even a few ratings are more valuable than metadata.  Proceedings of the 3rd ACM Conference on Recommender Systems, RecSys (2009).

Abstract: The Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by finding a linear transformation that transforms user or item descriptions so that they are as close as possible to the feature vectors generated by MF for CF. We propose methods for explicit feedback that are able to handle 140,000 features when feature vectors are very sparse. With movie metadata collected for the NP movies we show that the prediction performance of the methods is comparable to that of CF, and can be used to predict user preferences on new movies. We also investigate the value of movie metadata compared to movie ratings in regards of predictive power. We compare our solely CBF approach with a simple baseline rating-based predictor. We show that even 10 ratings of a new movie are more valuable than its metadata for predicting user ratings.

Gábor Takács, István Pilászy, Bottyán Németh, and Domonkos Tikk. Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research, 10 (2009).

Abstract: The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.