Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias users’ preference ratings after item consumption in the direction of the predicted rating. Top-N lists represent another common approach for presenting item recommendations in recommender systems. Through three controlled laboratory experiments, we show that top-N lists do not induce a discernible bias in user preference judgments. This result is robust, holding for both lists of personalized item recommendations and lists of items that are top-rated based on averages of aggregate user ratings. Adding numerical ratings to the list items does generate a bias, consistent with earlier studies. Thus, in contexts where preference biases are of concern to an online retailer or platform, top-N lists, without numerical predicted ratings, would be a promising format for displaying item recommendations.

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Web Usage Mining Approaches for Web Page Recommendation

Intelligent Techniques in Recommendation Systems ◽

10.4018/978-1-4666-2542-6.ch014 ◽

2013 ◽

pp. 271-288

Author(s):

H. Inbarani ◽

K. Thangavel

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Decision Rule ◽

Web Usage Mining ◽

Web Page ◽

Web Personalization ◽

Rule Based ◽

Web Usage ◽

User Ratings ◽

Content Based Filtering

The technology behind personalization or Web page recommendation has undergone tremendous changes, and several Web-based personalization systems have been proposed in recent years. The main goal of Web personalization is to dynamically recommend Web pages based on online behavior of users. Although personalization can be accomplished in numerous ways, most Web personalization techniques fall into four major categories: decision rule-based filtering, content-based filtering, and collaborative filtering and Web usage mining. Decision rule-based filtering reviews users to obtain user demographics or static profiles, and then lets Web sites manually specify rules based on them. It delivers the appropriate content to a particular user based on the rules. However, it is not particularly useful because it depends on users knowing in advance the content that interests them. Content-based filtering relies on items being similar to what a user has liked previously. Collaborative filtering, also called social or group filtering, is the most successful personalization technology to date. Most successful recommender systems on the Web typically use explicit user ratings of products or preferences to sort user profile information into peer groups. It then tells users what products they might want to buy by combining their personal preferences with those of like-minded individuals. However, collaborative filtering has limited use for a new product that no one has seen or rated, and content-based filtering to obtain user profiles might miss novel or surprising information. Additionally, traditional Web personalization techniques, including collaborative or content-based filtering, have other problems, such as reliance on subject user ratings and static profiles or the inability to capture richer semantic relationships among Web objects. To overcome these shortcomings, the new Web personalization tool, nonintrusive personalization, attempts to increasingly incorporate Web usage mining techniques. Web usage mining can help improve the scalability, accuracy, and flexibility of recommender systems. Thus, Web usage mining can reduce the need for obtaining subjective user ratings or registration-based personal preferences. This chapter provides a survey of Web usage mining approaches.

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Aspect-Oriented Recommender Systems

Designing, Engineering, and Analyzing Reliable and Efficient Software ◽

10.4018/978-1-4666-2958-5.ch004 ◽

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.

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Content-Enhanced Matrix Factorization for Recommender Systems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.1084 ◽

2013 ◽

Vol 475-476 ◽

pp. 1084-1089

Author(s):

Hui Yuan Chang ◽

Ding Xia Li ◽

Qi Dong Liu ◽

Rong Jing Hu ◽

Rui Sheng Zhang

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Factor Models ◽

User Preferences ◽

Latent Factor ◽

Successful Approach ◽

Latent Factor Models ◽

User Ratings ◽

Important Measurement ◽

Potential Customers

Recommender systems are widely employed in many fields to recommend products, services and information to potential customers. As the most successful approach to recommender systems, collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. It can be divided into two main braches - the neighbourhood approach (NB) and latent factor models. Some of the most successful realizations of latent factor models are based on matrix factorization (MF). Accuracy is one of the most important measurement criteria for recommender systems. In this paper, to improve accuracy, we propose an improved MF model. In this model, we not only consider the latent factors describing the user and item, but also incorporate content information directly into MF.Experiments are performed on the Movielens dataset to compare the present approach with the other method. The experiment results indicate that the proposed approach can remarkably improve the recommendation quality.

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User-Involved Preference Elicitation for Product Search and Recommender Systems

AI Magazine ◽

10.1609/aimag.v29i4.2200 ◽

2008 ◽

Vol 29 (4) ◽

pp. 93 ◽

Cited By ~ 30

Author(s):

Pearl Pu ◽

Li Chen

Keyword(s):

Recommender Systems ◽

State Of The Art ◽

Preference Elicitation ◽

Design Guidelines ◽

User Preferences ◽

The State ◽

User Preference ◽

Large Collection ◽

Complete Model ◽

Product Search

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state-of-the-art of the developed techniques and discuss concrete scenarios where they have been applied and tested.

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Investigation About Applicability of Personal Values for Recommender System

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2012.p0404 ◽

2012 ◽

Vol 16 (3) ◽

pp. 404-411 ◽

Cited By ~ 5

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.

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APPLYING k-SEPARABILITY TO COLLABORATIVE RECOMMENDER SYSTEMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213012500017 ◽

2012 ◽

Vol 21 (01) ◽

pp. 1250001

Author(s):

GEORGIOS ALEXANDRIDIS ◽

GEORGIOS SIOLAS ◽

ANDREAS STAFYLOPATIS

Keyword(s):

Recommender Systems ◽

Network Architecture ◽

Noisy Data ◽

Singular Value ◽

User Preferences ◽

Limited Information ◽

Neural Network Architecture ◽

Artificial Neural Network Architecture ◽

Value Decomposition ◽

User Ratings

Most recommender systems have too many items to propose to too many users based on limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This paper outlines a Collaborative Filtering Recommender System that tries to amend this situation. After applying Singular Value Decomposition to reduce the dimensionality of the data, our system makes use of a dynamic Artificial Neural Network architecture with boosted learning to predict user ratings. Furthermore we use the concept of k-separability to deal with the resulting noisy data, a methodology not yet tested in Recommender Systems. The combination of these techniques applied to the MovieLens datasets seems to yield promising results.

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User Preference and Reviews Analysis with Neural Networks for Travel Recommender Systems

International Journal of Engineering Research and Technology ◽

10.37624/ijert/13.8.2020.1896-1900 ◽

2020 ◽

Vol 13 (8) ◽

pp. 1896

Author(s):

S. Bairavel ◽

M. Krishnamurthy

Keyword(s):

Neural Networks ◽

Recommender Systems ◽

User Preference ◽

Travel Recommender Systems

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An Efficient and Intelligent Recommender System for Mobile Platform

Mehran University Research Journal of Engineering and Technology ◽

10.22581/muet1982.1804.02 ◽

2018 ◽

Vol 37 (4) ◽

pp. 463-480

Author(s):

Muhammad Jabbar ◽

Qaisar Javaid ◽

Muhammad Arif ◽

Asim Munir ◽

Ali Javed

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Past History ◽

Subjective Evaluation ◽

Mobile Platform ◽

Value Addition ◽

Hybrid Scheme ◽

Hybrid Approaches ◽

User Ratings ◽

Hybrid Recommendation

Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.

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