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Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
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Cited by 1490 (23 self)
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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Context-aware recommender systems.
- In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys ’08,
, 2008
"... Abstract This chapter aims to provide an overview of the class of multi-criteria recommender systems. First, it defines the recommendation problem as a multi-criteria decision making (MCDM) problem, and reviews MCDM methods and techniques that can support the implementation of multi-criteria recomm ..."
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Cited by 162 (29 self)
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Abstract This chapter aims to provide an overview of the class of multi-criteria recommender systems. First, it defines the recommendation problem as a multi-criteria decision making (MCDM) problem, and reviews MCDM methods and techniques that can support the implementation of multi-criteria recommenders. Then, it focuses on the category of multi-criteria rating recommenders -techniques that provide recommendations by modelling a user's utility for an item as a vector of ratings along several criteria. A review of current algorithms that use multicriteria ratings for calculating predictions and generating recommendations is provided. Finally, the chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating recommenders.
Tag-aware recommender systems by fusion of collaborative filtering algorithms
- In Proceedings of the 2nd ACM Symposium on Applied Computing
, 1995
"... Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content informa ..."
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Cited by 84 (3 self)
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Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are “global ” descriptions of items, tags are “local ” descriptions of items given by the users. To the best of our knowledge, there hasn’t been any prior study on tagaware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three twodimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering
- In Proceedings of the fourth ACM conference on Recommender systems
, 2010
"... Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we intro ..."
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Cited by 77 (4 self)
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Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide contextaware recommendations. We provide an algorithm to address the N-dimensional factorization, and show that the Multiverse Recommendation improves upon non-contextual Matrix Factorization up to 30 % in terms of the Mean Absolute Error (MAE). We also compare to two state-of-the-art context-aware methods and show that Tensor Factorization consistently outperforms them both in semi-synthetic and real-world data – improvements range from 2.5 % to more than 12 % depending on the data. Noticeably, our approach outperforms other methods by a wider margin whenever more contextual information is available.
Travel recommender systems
- IEEE Intelligent Systems
"... Mobile phones are becoming a primary platform for information access and when coupled with recommender systems technologies they can become key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile systems providing personal ..."
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Cited by 68 (15 self)
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Mobile phones are becoming a primary platform for information access and when coupled with recommender systems technologies they can become key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile systems providing personalized and more focussed content, hence limiting the negative effects of information overload. In this paper we review the major issues and opportunities that the mobile scenario opens to the application of recommender systems especially in the area of travel and tourism. We overview major techniques that have been proposed in the last years and we illustrate the supported functions. We also illustrate specific computational models that have been proposed for mobile recommender systems and we close the paper by presenting some possible future developments and extension in this area. 1
Introduction to Recommender Systems Handbook
- RECOMMENDER SYSTEMS HANDBOOK
, 2011
"... Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly ..."
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Cited by 56 (5 self)
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Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly
Mediation of User Models for Enhanced Personalization in Recommender Systems
- IN: USER MODEL, USER-ADAPT. INTERACT
, 2008
"... Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This paper proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other rec ..."
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Cited by 50 (7 self)
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Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This paper proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The paper discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and crossrepresentation. Finally, the paper reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users.
Towards time-dependant recommendation based on implicit feedback
- In Workshop on context-aware recommender systems (CARSâ Ă´ Z09
, 2009
"... Context-aware recommender systems (CARS) aim at im-proving users ’ satisfaction by tailoring recommendations to each particular context. In this work we propose a con-textual pre-filtering technique based on implicit user feed-back. We introduce a new context-aware recommendation approach called use ..."
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Cited by 48 (3 self)
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Context-aware recommender systems (CARS) aim at im-proving users ’ satisfaction by tailoring recommendations to each particular context. In this work we propose a con-textual pre-filtering technique based on implicit user feed-back. We introduce a new context-aware recommendation approach called user micro-profiling. We split each single user profile into several possibly overlapping sub-profiles, each representing users in particular contexts. The predic-tions are done using these micro-profiles instead of a single user model. The users ’ taste can depend on the exact partition of the contextual variable. The identification of a meaningful par-tition of the users ’ profile and its evaluation is a non-trivial task, especially when using implicit feedback and a contin-uous contextual domain. We propose an off-line evaluation procedure for CARS in these conditions and evaluate our approach on a time-aware music recommendation sytem. 1.
Using context to improve predictive modeling of customers in personalization applications
- Knowledge and Data Engineering, IEEE Transactions on
, 2008
"... Abstract—The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been d ..."
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Cited by 44 (5 self)
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Abstract—The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is “cleverly ” modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers ’ behavior and decreasing the costs of gathering contextual information. Index Terms—Personalization, context, data mining, user modeling, predictive modeling. Ç 1
Matrix factorization techniques for context aware recommendation
- In ACM RecSys
, 2011
"... Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel contextaware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item rat ..."
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Cited by 32 (4 self)
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Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel contextaware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.
