Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a user will like a manga that received few ratings from the community? Content-based techniques can alleviate this issue but require extra information, that is usually expensive to gather. In this paper, we use a deep learning technique, Illustration2Vec, to easily extract tag information from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE (Blended Alternate Least Squares with Explanation), a new model for collaborative filtering, that benefits from this extra information to recommend mangas. We show, using real data from an online manga recommender system called Mangaki, that our model improves substantially the quality of recommendations, especially for less-known manga, and is able to provide an interpretation of the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop, co-located with ICDAR 2017 in Kyoto on November 10, 201

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Social and content hybrid image recommender system for mobile social networks

One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

Building and exploiting context on the web

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Personalized Image Search Using Complex Multiple Words Based Queries

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Exploring Methods to Persuade Users to watch Social Problem Films through the Case Study of Race-related Films

The present study reports on the success of persuasive techniques and recommendation engine on persuading participants to choose a race-related movie. The study used a technical component as well as an experimental study to see how users respond to certain persuasive techniques. The technical component included a content-based filtering recommendation engine to recommend lists of movies to participants based on films they already enjoy. Persuasive techniques were then applied to those lists and results were measured and analyzed. This project aims to explore the relationship that recommendation engines and persuasive techniques have on users and their willingness to branch out in the content they consume. This study allowed me to get a baseline for how users respond to certain techniques if this study were to be done on a larger scale