Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

構造化データに対する予測手法：グラフ，順序，時系列

京都大学新制・課程博士博士(情報学)甲第23439号情博第769号新制||情||131(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 阿久津 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Towards Scalable Personalization

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most relevant items to their users. The most prominent challenges underlying personalization are: scalability, privacy, and heterogeneity. Scalability is challenging given the growing rate of the Internet and its dynamics, both in terms of churn (i.e., users might leave/join at any time) and changes of user interests over time. Privacy is also a major concern as users might be reluctant to expose their profiles to unknown parties (e.g., other curious users), unless they have an incentive to significantly improve their navigation experience and sufficient guarantees about their privacy. Heterogeneity poses a major technical difficulty because, to be really meaningful, the profiles of users should be extracted from a number of their navigation activities (heterogeneity of source domains) and represented in a form that is general enough to be leveraged in the context of other applications (heterogeneity of target domains). In this dissertation, we address the above-mentioned challenges. For scalability, we introduce democratization and incrementality. Our democratization approach focuses on iteratively offloading the computationally expensive tasks to the user devices (via browsers or applications). This approach achieves scalability by employing the devices of the users as additional resources and hence the throughput of the approach (i.e., number of updates per unit time) scales with the number of users. Our incrementality approach deals with incremental similarity metrics employing either explicit (e.g., ratings) or implicit (e.g., consumption sequences for users) feedback. This approach achieves scalability by reducing the time complexity of each update, and thereby enabling higher throughput. We tackle the privacy concerns from two perspectives, i.e., anonymity from either other curious users (user-level privacy) or the service provider (system-level privacy). We strengthen the notion of differential privacy in the context of recommenders by introducing distance-based differential privacy (D2P) which prevents curious users from even guessing any category (e.g., genre) in which a user might be interested in. We also briefly introduce a recommender (X-REC) which employs uniform user sampling technique to achieve user-level privacy and an efficient homomorphic encryption scheme (X-HE) to achieve system-level privacy. We also present a heterogeneous recommender (X-MAP) which employs a novel similarity metric (X-SIM) based on paths across heterogeneous items (i.e., items from different domains). To achieve a general form for any user profile, we generate her AlterEgo profile in a target domain by employing an item-to-item mapping from a source domain (e.g., movies) to a target domain (e.g., books). Moreover, X-MAP also enables differentially private AlterEgos. While X-MAP employs user-item interactions (e.g., ratings), we also explore the possibility of heterogeneous recommendation by using content-based features of users (e.g., demography, time-varying preferences) or items (e.g., popularity, price)

A Survey on Trust Computation in the Internet of Things

Internet of Things defines a large number of diverse entities and services which interconnect with each other and individually or cooperatively operate depending on context, conditions and environments, produce a huge personal and sensitive data. In this scenario, the satisfaction of privacy, security and trust plays a critical role in the success of the Internet of Things. Trust here can be considered as a key property to establish trustworthy and seamless connectivity among entities and to guarantee secure services and applications. The aim of this study is to provide a survey on various trust computation strategies and identify future trends in the field. We discuss trust computation methods under several aspects and provide comparison of the approaches based on trust features, performance, advantages, weaknesses and limitations of each strategy. Finally the research discuss on the gap of the trust literature and raise some research directions in trust computation in the Internet of Things

Social-media monitoring for cold-start recommendations

Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem

Could Alexa Increase Your Social Worth?

People have historically used personal introductions to build social capital, which is the foundation of career networking and is perhaps the most effective way to advance a career (Lin, 2001). With societal changes, such as the pandemic (Venkatesh & Edirappuli, 2020), and the increasing capabilities of Artificial Intelligence (AI), new approaches may emerge that impact societal relationships. Social capital theory highlights the need for reciprocal agreements to establish the trust between parties (Gouldner, 1960). My theoretical prediction and focus of this research include two principles: The impact of reciprocity in evaluating trust of the source of the introduction and the acceptability of AI in interpersonal relationships. I test this relationship through the creation of plausible vignettes that the participants may have encountered in business. The results show that a higher trust of AI and could replace one side of the relationship, thus reducing the dependency on or eliminating reciprocal behavior