A Hybrid CBR Approach for the Long Tail Problem in Recommender Systems

2017 ◽  
pp. 35-45 ◽  
Author(s):  
Gharbi Alshammari ◽  
Jose L. Jorro-Aragoneses ◽  
Stelios Kapetanakis ◽  
Miltos Petridis ◽  
Juan A. Recio-García ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Long Tail

scholarly journals A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems

2022 ◽  
Vol 40 (2) ◽  
pp. 1-31
Author(s):  
Masoud Mansoury ◽  
Himan Abdollahpouri ◽  
Mykola Pechenizkiy ◽  
Bamshad Mobasher ◽  
Robin Burke
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
System Level ◽  
End User ◽  
Critical System ◽  
Long Tail ◽  
Fair Representation ◽  
Low Visibility ◽  
The Subject ◽  
Low Exposure

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increase aggregate diversity to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this article, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high-quality items that have low visibility or items from suppliers with low exposure to the users’ final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, although it significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.

Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation

2018 ◽  
Vol 22 (4) ◽  
pp. 506-513 ◽  
Author(s):  
Yasufumi Takama ◽  
◽  
Yu-Sheng Chen ◽  
Ryori Misawa ◽  
Hiroshi Ishikawa
Keyword(s):  
Recommender Systems ◽  
User Modeling ◽  
Personal Values ◽  
Experimental Result ◽  
Long Tail ◽  
Recommendation Algorithms

This paper examines the potential of personal values-based user modeling for long tail item recommendation. Long tail items are defined as those which are not popular but are preferred by small numbers of specific users. Although recommending long tail items to relevant users is beneficial for both the providers and consumers of such items, it is known to be a challenge for most recommendation algorithms. In particular, a long tail item is one that would be purchased and/or rated by a small number of users, so it is difficult to predict its rating accurately. This paper assumes that the influence of personal values becomes more obvious when users evaluate long tail items, and examines it through offline experiment. The Rating Matching Rate (RMRate) has been proposed in order to incorporate users’ personal values into recommender systems. As the RMRate models personal values as the weight of an item’s attribute, it is easy to incorporate into existing recommendation algorithms. An experiment was conducted to evaluate the performance of long tail item recommendation; Experimental result shows that personal values-based user modeling can recommend less popular items while maintaining precision.

scholarly journals Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 439 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Vivian López Batista ◽  
M. Dolores Muñoz Vicente ◽  
Ángel Luis Sánchez Lázaro ◽  
María N. Moreno-García
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
User Behavior ◽  
Error Rates ◽  
User Preferences ◽  
Music Recommendation ◽  
Power Law Distribution ◽  
Long Tail ◽  
Music Recommender Systems ◽  
Structure Metrics

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.

A Recommendation Algorithm from the Object Perspective

2014 ◽  
Vol 687-691 ◽  
pp. 2664-2667
Author(s):  
Wei Zeng ◽  
Ya Fan ◽  
Bao Zhuo Zhou ◽  
Qing Xian Wang
Keyword(s):  
Recommender Systems ◽  
Information Filtering ◽  
Relevant Information ◽  
Sufficient Data ◽  
Recommendation Algorithm ◽  
Long Tail

The purpose of designing recommender systems is to help individual users find relevant information. However, many recommender systems have been facing the challenges of finding niche objects, which users may like but difficult to find due to the lack of sufficient data. In this paper, we propose a recommendation algorithm which takes a niche object as input and outputs a list of users who may be interested it. By this approach, every niche object can be recommended at least one time. Further analysis indicates that those niche objects are usually collected by active users and the owners who are very similar to each other. Therefore, this work has outlined the significant relevance with the challenge, the Long Tail problem, and provided a different perspective to solve it in the field of information filtering.

Personal Values-Based Item Modeling and its Application to Recommendation with Explanation

2016 ◽  
Vol 20 (6) ◽  
pp. 867-874 ◽  
Author(s):  
Yasufumi Takama ◽  
◽  
Takayuki Yamaguchi ◽  
Shunichi Hattori ◽  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Modeling ◽  
Personal Values ◽  
Long Tail ◽  
Factors Affecting ◽  
Personal Value ◽  
Recommendation Accuracy ◽  
User Experiment

This paper proposes a personal-value based item modeling, which is used for calculating predicted ratings and for explaining recommendation. Personal value is one of factors affecting our decision making, and its application to recommender systems has been studied recently. This paper extends existing personal values-based user modeling to item modeling, which estimates characteristics of reviewers who like / dislike target items. A method for calculating predicted ratings based on obtained personal values-based item models is also proposed. Furthermore, this paper focuses on explanation of recommendation as well, which is one of challenges in the recent study of recommender systems. Improvements of user’s satisfactions for recommender systems by showing process of recommendation gets to be important in addition to precision of recommendation. A recommender system is developed based on the proposed method, of which effectiveness is evaluated by a user experiment, in which the target items are movies. Experimental results showed the effectiveness of the proposed method including recommendation accuracy and an explanation of recommendation. It is also shown that the proposed recommender system has the potential to recommend long-tail items.

Long-Tail Data-Driven Recommendations - Innovative Solutions for Financial Recommender Systems

2021 ◽  
Author(s):  
Mariia V. Sigova ◽  
Igor K. Klioutchnikov ◽  
Oleg I. Klioutchnikov
Keyword(s):  
Recommender Systems ◽  
Data Driven ◽  
Long Tail ◽  
Innovative Solutions
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