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.

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 User Modeling from Review Browsing History for Personal Values-Based Recommendation

2020 ◽  
Vol 24 (3) ◽  
pp. 326-334
Author(s):  
Yasufumi Takama ◽  
◽  
Suzuto Shimizu
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
User Modeling ◽  
Personal Values ◽  
User Model ◽  
User Feedback ◽  
Modeling Method ◽  
History Of

This paper proposes a personal values-based user modeling method from user’s browsing history of reviews. Personal values-based user modeling and its application to recommender systems have been studied. This approach models users’ personal values as the effect of item’s attributes on their decision making. While existing method obtains a user model from reviews posted by a user, this paper proposes to obtain it from reviews a user consulted for his/her decision making. Methods for determining reviews to present for obtaining user feedback, as well as for selecting items to recommend are proposed, of which effectiveness are shown with user experiments.

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

Multi-Criteria Recommender Systems

2017 ◽  
Vol 8 (4) ◽  
pp. 20-48 ◽  
Author(s):  
Ferdaous Hdioud ◽  
Bouchra Frikh ◽  
Brahim Ouhbi ◽  
Ismail Khalil
Keyword(s):  
Decision Making ◽  
Active Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Selection Method ◽  
Item Selection ◽  
Multi Criteria Decision Making ◽  
Selection Approach ◽  
Comprehensive Study

A Recommender System (RS) works much better for users when it has more information. In Collaborative Filtering, where users' preferences are expressed as ratings, the more ratings elicited, the more accurate the recommendations. New users present a big challenge for a RS, which has to providing content fitting their preferences. Generally speaking, such problems are tackled by applying Active Learning (AL) strategies that consist on a brief interview with the new user, during which she is asked to give feedback about a set selected items. This article presents a comprehensive study of the most important techniques used to handle this issue focusing on AL techniques. The authors then propose a novel item selection approach, based on Multi-Criteria ratings and a method of computing weights of criteria inspired by a multi-criteria decision making approach. This selection method is deployed to learn new users' profiles, to identify the reasons behind which items are deemed to be relevant compared to the rest items in the dataset.

Investigation About Applicability of Personal Values for Recommender System

2012 ◽  
Vol 16 (3) ◽  
pp. 404-411 ◽  
Author(s):  
Shunichi Hattori ◽  
◽  
Yasufumi Takama
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Personal Values ◽  
User Preference

A recommender systemis a fundamental technique for finding information that is likely to be preferred by users among vast amounts of information. While existing recommender systems usually employ user preference or attributes of items to make recommendations, marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. This paper investigates the applicability of personal values in modeling items and users. The results of questionnaires show the feasibility of a recommender system based on personal values.

scholarly journals Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 41 ◽  
Author(s):  
Iftikhar Alam ◽  
Shah Khusro ◽  
Mumtaz Khan
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Technological Advancement ◽  
Factors Affecting ◽  
Web Contents ◽  
Smart Tv ◽  
Group Members ◽  
Single User ◽  
Subjective Study ◽  
The Web

The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s).

Classification of Informative Reviews Based on Personal Values

2014 ◽  
Vol 18 (3) ◽  
pp. 331-339
Author(s):  
Yasufumi Takama ◽  
◽  
Zhongjie Mao ◽  
Shunichi Hattori
Keyword(s):  
Decision Making ◽  
Personal Values ◽  
The Other ◽  
Independent Factor ◽  
Vast Amount ◽  
Personal Value ◽  
Other Hand ◽  
Average Accuracy ◽  
Copy And Paste

This paper proposes a method for classifying informative reviews based on personal values. Reviews of an item are useful for a user who is considering purchasing it. However, it is difficult for readers to find informative reviews from vast amount of reviews because of existence of too many uninformative reviews. This paper supposes that the value of a review is affected by reader-dependent and independent factors. Typical uninformative reviews in terms of reader-independent factor are copy-and-paste reviews, which do not provide any readers with useful information for their decision-making. On the other hand, it is supposed different readers regard different reviews as informative, which is affected by their personal values. This paper focuses on such a readerdependent factor, and proposes a methods for classifying informative reviews based on reader’s personal value. Experiments are conducted using actual review data provided by Rakuten Inc., of which the results show about 0.7 of average accuracy is achieved. Furthermore, it is also shown proposed method can model judging criteria common to those who have similar personal values.

scholarly journals The effect of visualisation on user experience in recommender systems

2021 ◽  
Vol 26 (3) ◽  
Author(s):  
Wen-Yau Liang ◽  
Chun-Che Huang ◽  
Tzu-Liang Tseng ◽  
Zih-Yan Wang ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
User Satisfaction ◽  
Business Environment ◽  
Perceived Usefulness ◽  
Continuance Intention ◽  
Systems Research ◽  
Proposed Model

Introduction. Measuring user experience, though natural in a business environment, is often challenging for recommender systems research. How recommender systems can substantially improve consumers’ decision making is well understood; but the influence of specific design attributes of the recommender system interface on decision making and other outcome measures is far less understood. Method. This study provides the first empirical test of post-acceptance model adaption for information system continuance in the context of recommender systems. Based on the proposed model, two presentation types (with or without using tag cloud) are compared. An experimental design is used and a questionnaire is developed to analyse the data. Analysis. Data were analysed using SPSS and SmartPLS (partial least squares path modeling method). Statistical methods used for the questionnaire on user satisfaction were a reliability analysis, a validity analysis and T-tests. Results. The results demonstrate that the proposed model is supported and that the visual recommender system can indeed significantly enhance user satisfaction and continuance intention. Conclusions. In order to improve the satisfaction or continuance intention of users, it is required to improve the perceived usefulness, effectiveness and visual attractiveness of a recommender system.

scholarly journals A User-Centric Housing Recommender System

2018 ◽  
Vol 10 (3) ◽  
pp. 17-24
Author(s):  
Ojokoh B.A. ◽  
Olayemi O.C. ◽  
Babalola A.E. ◽  
Eyo E.O.
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Choice Making ◽  
Decision Making Processes ◽  
User Centric ◽  
Cost Implications ◽  
House Buyers

 Recommender systems are very useful in assisting users to reduce the complexities involved in their decision making processes. It is particularly difficult for people to make decisions on housing choices because different options exist with different facilities, in different locations and with varied cost implications. This paper proposes a hybrid user-centric housing recommender system that is implemented to assist potential house buyers and tenants to generate house listings based on their preferences with the aid of fuzzy logic and item-based collaborative filtering. A virtual tour of the houses is also provided for better choice making.   

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