Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset

Multiple social network integration framework for recommendation across system domain

A recommender system is a special software that recommends items to a user based on the user’s history. A recommender system comprises users, items and a rating matrix. Rating matrix stores the interactions between users and items. The system faces a variety of problems among which three are the main concerns of this research. These problems are cold start, sparsity, and diversity. Majority of the research use a conventional framework for solving these problems. In a conventional recommender system, user profiles are generated from a single feedback source, whereas, Cross Domain Recommender Systems (CDRS) research relies on more than one source. Recently researchers have started using “Social Network Integration Framework”, that integrates social network as an additional feedback source. Although the existing framework alleviates recommendation problems better than the conventional framework, it still faces limitations. Existing framework is designed only for a single source domain and requires the same user participation in both the source and the target domain. Existing techniques are also designed to integrate knowledge from one social network only. To integrate multiple sources, this research developed a “Multiple Social Network Integration Framework”, that consists of two models and three techniques. Firstly, the Knowledge Generation Model generates interaction matrices from “n” number of source domains. Secondly, the Knowledge Linkage Model links the source domains to the target domain. The outputs of the models are inputs of the techniques. Then multiple techniques were developed to address cold start, sparsity and diversity problem using multiple source networks. Three techniques addressed the cold start problem. These techniques are Multiple Social Network integration with Equal Weights Participation (MSN-EWP), Multiple Social Network integration with Local Adjusted Weights Participation (MSNLAWP) and Multiple Social Network integration with Target Adjusted Weights Participation (MSN-TAWP). Experimental results showed that MSN-TAWP performed best by producing 47% precision improvement over popularity ranking as the baseline technique. For the sparsity problem, Multiple Social Network integration for K Nearest Neighbor identification (MSN-KNN) technique performed at least 30% better in accuracy while decreasing the error rate by 20%. Diversity problem was addressed by two combinations of the cold start and sparsity techniques. These combinations, EWP + MSN-KNN, TAWP + MSN-KNN and TAWP + MSN-KNN outperformed the rest of the diversity combinations by 56% gain in diversity with a precision loss of 1%. In conclusion, the techniques designed for multiple sources outperformed existing techniques for addressing cold start, sparsity and diversity problem. Finally, an extension of multiple social network integration framework for content-based and hybrid recommendation techniques should be considered future work

Enhancing Collaborative Filtering Using Implicit Relations in Data

International audienceThis work presents a Recommender System (RS) that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion (explicit data) and navigating through the web-page (implicit data). The Matrix Fac-torization (MF) recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in an independent layer. Indeed, using past preferences, we deeply analyze the implicit interest of users in the attributes of items. By using this, we transform ratings and predictions into " semantic values " , where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database. We compare our work against a simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be par-allelized, alleviating time processing in large amount of data

Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems

The Recommender system is a vital information service on today's Internet. Recently, graph neural networks have emerged as the leading approach for recommender systems. We try to review recent literature on graph neural network-based recommender systems, covering the background and development of both recommender systems and graph neural networks. Then categorizing recommender systems by their settings and graph neural networks by spectral and spatial models, we explore the motivation behind incorporating graph neural networks into recommender systems. We also analyze challenges and open problems in graph construction, embedding propagation and aggregation, and computation efficiency. This guides us to better explore the future directions and developments in this domain.Comment: arXiv admin note: text overlap with arXiv:2103.08976 by other author

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

FATREC Workshop on Responsible Recommendation Proceedings

We sought with this workshop, to foster a discussion of various topics that fall under the general umbrella of responsible recommendation: ethical considerations in recommendation, bias and discrimination in recommender systems, transparency and accountability, social impact of recommenders, user privacy, and other related concerns. Our goal was to encourage the community to think about how we build and study recommender systems in a socially-responsible manner. Recommendation systems are increasingly impacting people\u27s decisions in different walks of life including commerce, employment, dating, health, education and governance. As the impact and scope of recommendations increase, developing systems that tackle issues of fairness, transparency and accountability becomes important. This workshop was held in the spirit of FATML (Fairness, Accountability, and Transparency in Machine Learning), DAT (Data and Algorithmic Transparency), and similar workshops in related communities. With Responsible Recommendation , we brought that conversation to RecSys

USING META-DATA FROM FREE-TEXT USER-GENERATED CONTENT TO IMPROVE PERSONALIZED RECOMMENDATION BY REDUCING SPARSITY

Ph.DDOCTOR OF PHILOSOPH

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

Mobile App Recommendation

Ph.DDOCTOR OF PHILOSOPH