ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH AUXILIARY INFORMATION IMPUTATION

This dissertation studies the factors that negatively impact the accuracy of the collaborative filtering recommendation systems based on nonnegative matrix factorization (NMF). The keystone in the recommendation system is the rating that expresses the user\u27s opinion about an item. One of the most significant issues in the recommendation systems is the lack of ratings. This issue is called cold-start issue, which appears clearly with New-Users who did not rate any item and New-Items, which did not receive any rating. The traditional recommendation systems assume that users are independent and identically distributed and ignore the connections among users whereas the recommendation actually is a social activity. This dissertation aims to enhance NMF-based recommendation systems by utilizing the imputation method and limiting the errors that are introduced in the system. External information such as trust network and item categories are incorporated into NMF-based recommendation systems through the imputation. The proposed approaches impute various subsets of the missing ratings. The subsets are defined based on the total number of the ratings of the user or item before the imputation, such as impute the missing ratings of New-Users, New-Items, or cold-start users or items that suffer from the lack of the ratings. In addition, several factors are analyzed that affect the prediction accuracy when the imputation method is utilized with NMF-based recommendation systems. These factors include the total number of the ratings of the user or item before the imputation, the total number of imputed ratings for each user and item, the average of imputed rating values, and the value of imputed rating values. In addition, several strategies are applied to select the subset of missing ratings for the imputation that lead to increasing the prediction accuracy and limiting the imputation error. Moreover, a comparison is conducted with some popular methods that are in common with the proposed method in utilizing the imputation to handle the lack of ratings, but they differ in the source of the imputed ratings. Experiments on different large-size datasets are conducted to examine the proposed approaches and analyze the effects of the imputation on accuracy. Users and items are divided into three groups based on the total number of the ratings before the imputation is applied and their recommendation accuracy is calculated. The results show that the imputation enhances the recommendation system by capacitating the system to recommend items to New-Users, introduce New-Items to users, and increase the accuracy of the cold-start users and items. However, the analyzed factors play important roles in the recommendation accuracy and limit the error that is introduced from the imputation

Three Essays on Friend Recommendation Systems for Online Social Networks

Social networking sites (SNSs) first appeared in the mid-90s. In recent years, however, Web 2.0 technologies have made modern SNSs increasingly popular and easier to use, and social networking has expanded explosively across the web. This brought a massive number of new users. Two of the most popular SNSs, Facebook and Twitter, have reached one billion users and exceeded half billion users, respectively. Too many new users may cause the cold start problem. Users sign up on a SNS and discover they do not have any friends. Normally, SNSs solve this problem by recommending potential friends. The current major methods for friend recommendations are profile matching and “friends-of-friends.” The profile matching method compares two users’ profiles. This is relatively inflexible because it ignores the changing nature of users. It also requires complete profiles. The friends-of-friends method can only find people who are likely to be previously known to each other and neglects many users who share the same interests. To the best of my knowledge, existing research has not proposed guidelines for building a better recommendation system based on context information (location information) and user-generated content (UGC). This dissertation consists of three essays. The first essay focuses on location information and then develops a framework for using location to recommend friends--a framework that is not limited to making only known people recommendations but that also adds stranger recommendations. The second essay employs UGC by developing a text analytic framework that discovers users’ interests and personalities and uses this information to recommend friends. The third essay discusses friend recommendations in a certain type of online community – health and fitness social networking sites, physical activities and health status become more important factors in this case. Essay 1: Location-sensitive Friend Recommendations in Online Social Networks GPS-embedded smart devices and wearable devices such as smart phones, tablets, smart watches, etc., have significantly increased in recent years. Because of them, users can record their location at anytime and anyplace. SNSs such as Foursquare, Facebook, and Twitter all have developed their own location-based services to collect users’ location check-in data and provide location-sensitive services such as location-based promotions. None of these sites, however, have used location information to make friend recommendations. In this essay, we investigate a new model to make friend recommendations. This model includes location check-in data as predictors and calculates users’ check-in histories--users’ life patterns--to make friend recommendations. The results of our experiment show that this novel model provides better performance in making friend recommendations. Essay 2: Novel Friend Recommendations Based on User-generated Contents More and more users have joined and contributed to SNSs. Users share stories of their daily life (such as having delicious food, enjoying shopping, traveling, hanging out, etc.) and leave comments. This huge amount of UGC could provide rich data for building an accurate, adaptable, effective, and extensible user model that reflects users’ interests, their sentiments about different type of locations, and their personalities. From the computer-supported social matching process, these attributes could influence friend matches. Unfortunately, none of the previous studies in this area have focused on using these extracted meta-text features for friend recommendation systems. In this study, we develop a text analytic framework and apply it to UGCs on SNSs. By extracting interests and personality features from UGCs, we can make text-based friend recommendations. The results of our experiment show that text features could further improve recommendation performance. Essay 3: Friend Recommendations in Health/Fitness Social Networking Sites Thanks to the growing number of wearable devices, online health/fitness communities are becoming more and more popular. This type of social networking sites offers individuals the opportunity to monitor their diet process and motivating them to change their lifestyles. Users can improve their physical activity level and health status by receiving information, advice and supports from their friends in the social networks. Many studies have confirmed that social network structure and the degree of homophily in a network will affect how health behavior and innovations are spread. However, very few studies have focused on the opposite, the impact from users’ daily activities for building friendships in a health/fitness social networking site. In this study, we track and collect users’ daily activities from Record, a famous online fitness social networking sites. By building an analytic framework, we test and evaluate how people’s daily activities could help friend recommendations. The results of our experiment have shown that by using the helps from these information, friend recommendation systems become more accurate and more precise

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen

Early evaluation of Unistats: user experiences

This paper sets out the findings of the user evaluation of Unistats.UK Higher Education Funding Bodie