A Latent Factor Model for Board Recommendations in Pinterest

The past two years have seen the rise of a new online social network – Pinterest – which has grown more rapidly than any other social network (now reaching 70 million users). Pinterest is primarily organized around photos (or “pins”), where users reveal their interests via organizing pins into self-assigned categorical boards. However, one of the key challenges for new and existing users of Pinterest is to find boards of interest from the overall collection of 750 million boards. Hence, this thesis focuses on the problem of board recommendation in Pinterest towards identifying personalized, high-quality boards without requiring exhaustive search or browsing by the user. Board recommendation in Pinterest is challenging for a number of critical reasons: (i) Unlike community-oriented recommenders for movies, books, and other media, boards are highly personalized and not viewed or rated by many others. (ii) Many pins and boards lack descriptive text and other features that are typically used to power modern recommenders. (iii) Finally, evaluating the quality of a Pinterest board recommender is difficult, since there are no baseline nor ground truth recommendations of Pinterest to compare against. With these challenges in mind, this thesis proposes a new latent factor model for generating Pinterest board recommendations. To tackle the feature sparsity and personal boards challenges, the overall approach generates ratings for every user-board pair which is then fed to a latent factor model which factorizes the sparse matrix to give ratings for unrated user-board pairs and the top rated boards form the recommendation list. Two of the key components of the proposed latent factor model are the (i) definition of the universe of users around each target user for identifying candidate boards to recommend; and (ii) the approach for assigning implicit ratings to each user-board pair for this universe of users (as the basis of the latent factor model). For the first component, we investigate three universe types: a collection of randomly selected users, a collection of users in the target user's personal Pinterest network, and a collection of users who are “similar” to the target user. For the second component, we construct ratings via three approaches: a board-count method, a category-based method, and and LDA-based method. We investigate these design choices through a comprehensive set of experiments over a dataset of around 50,000 Pinterest users, 100 million pins, and around 570,000 boards

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

A Personalized Travel Recommendation System Using Social Media Analysis

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model

Detection of Trending Topic Communities: Bridging Content Creators and Distributors

The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users' interactions and the spread of new information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0