Approach For Recommender Systems Research Articles

A personal data store approach for recommender systems: enhancing privacy without sacrificing accuracy

Recommender systems have become extremely common in recent years, and are applied in a variety of domains. Existing recommender systems exhibit two major limitations: (1) Privacy - each service provider holds a database that contains information about all of its users; and (2) Partial view - when recommending to users, each such service can rely only on data that were collected by the service itself.The Open Personal Data Store (openPDS) architecture was recently suggested for storing personal data in a privacy preserving way. Inspired by openPDS, we suggest a novel architecture for recommender systems that overcomes the two limitations mentioned above. The suggested architecture allows the recommender system to utilize rich data collected about the user (possibly through other services) to produce more accurate recommendations, while allowing its users to manage and gain control over their own data.We evaluate the suggested architecture on two different use cases: movies and web browsing, and compare its performance with that of a popular non-privacy-aware collaborative-filtering algorithm. We find that in comparison to the alternative approach, our approach is able to enhance privacy significantly without sacrificing the accuracy level of the recommendations (and in some cases providing even higher level of accuracy).

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s).

Evolutionary approaches for weight optimization in collaborative filtering-basedrecommender systems

Collaborative filtering is one of the widely adopted approaches in recommender systems used for e-commerce applications, stating that users having similar tastes will have similar preferences in the future. The literature presents a number of similarity metrics such as the extended Jaccard coefficient to quantify these preference similarities. This paper aims to improve prediction accuracy by optimizing the similarity values computed using these metrics by adopting two biologically inspired approaches, namely artificial bee colony and genetic algorithms, with a bottom-up approach, suggesting that any improvement on a single-user basis will reflect on the overall prediction accuracy. Detailed statistical analysis was performed using the t-test, analysis of variance, and McNemar's test to see whether there were performance differences. The results show that statistically significant differences exist with high confidence levels.

The Computational Approach for Recommendation System Based on Tagging Data

Recommendation approaches like a platform for learning algorithm. We can use some predicted values to put them in the data pipeline forlearning. There is a hard nuance of how to calculate the similarity measurewhen we have a small number of actions at all, its not a new user or item to use cold start methods, we just have not enough quantity to say it may be interpreted like regularity. The frequency of tags what we would have fromusers will have a huge impact to predict his future taste. The article describes created a computational approach using as explicit and as implicit feedbacks from users and evaluates tags by Jaccard distance to resolve this issue. To compare results with existed numerical methods there is a comparison table that shows the high quality of the proposed approach.

A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems

A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems

Multi-Criteria Review-Based Recommender System–The State of the Art

In recent times, the recommender systems (RSs) have considerable importance in academia, commercial activities, and industry. They are widely used in various domains such as shopping (Amazon), music (Pandora), movies (Netflix), travel (TripAdvisor), restaurant (Yelp), people (Facebook), and articles (TED). Most of the RSs approaches rely on a single-criterion rating (overall rating) as a primary source for the recommendation process. However, the overall rating is not enough to gain high accuracy of recommendations because the overall rating cannot express fine-grained analysis behind the user’s behavior. To solve this problem, multi-criteria recommender systems (MCRSs) have been developed to improve the accuracy of the RS performance. Additionally, a new source of information represented by the user-generated reviews is incorporated in the recommendation process because of the rich and numerous information included (i.e. review elements) related to the whole item or to a certain feature of the item or the user’s preferences. The valuable review elements are extracted using either text mining or sentiment analysis. MCRSs benefit from the review elements of the user-generated reviews in building their criteria forming multi-criteria review based recommender systems. The review elements improve the accuracy of the RS performance and mitigate most of the RS’s problems such as the cold start and sparsity. In this review, we focused on the multi-criteria review-based recommender system and explained the user reviews elements in detail and how these can be integrated into the RSs to help develop their criteria to enhance the RSs performance. Finally, based on the survey, we presented four future trends based on this type of RSs to support researchers who wish to pursue studies in this area.

Community Detection-Based Recommendation Framework

Recommender system is a type of information filtering system predicting users preferences about items, aiming to generate personalized recommendations. Various recommendation approaches exist in the literature that differ in terms of methodology and types of systems they can be utilized on. In recent years some attempts have been made to incorporate community detection methods into recommender systems to make the process of recommendation generation more accurate in terms of rating or preference prediction and efficient in terms of computational complexity. In this paper we propose a community detection-based approach for recommender system, which is more reasonable in certain applications.

MF-NB Learning Based Approach for Recommendation System

MF-NB Learning Based Approach for Recommendation System

A study of the applicability of recommender systems for the Production and Distributed Analysis system PanDA of the ATLAS Experiment

Scientific computing has advanced in the ways it deals with massive amounts of data, since the production capacities have increased significantly for the last decades. Most large science experiments require vast computing and data storage resources in order to provide results or predictions based on the data obtained. For scientific distributed computing systems with hundreds of petabytes of data and thousands of users it is important to keep track not just of how data is distributed in the system, but also of individual users’ interests in the distributed data (reveal implicit interconnection between user and data objects). This however requires the collection and use of specific statistics such as correlations between data distribution, the mechanics of data distribution, and mainly user preferences. This work focuses on user activities (specifically, data usages) and interests in such a distributed computing system, namely PanDA (Production ANd Distributed Analysis system). PanDA is a high-performance workload management system originally designed to meet production and analysis requirements for a data-driven workload at the Large Hadron Collider Computing Grid for the ATLAS Experiment hosted at CERN (the European Organization for Nuclear Research). In this work we are going to investigate whether data collection that was gathered in the past in PanDA shows any trends indicating that users could have mutual interests that would be kept for the next data usages (i.e., data usage patterns), using data mining techniques such as association analysis, sequential pattern mining, and basics of the recommender system approach. We will show that such common interests between users indeed exist and thus could be used to provide recommendations (in terms of the collaborative filtering) to help users with their data selection process.

Boosting collaborative filtering with an ensemble of co-trained recommenders

Collaborative Filtering (CF) is one of the best performing and most widely used approaches for recommender systems. Although significant progress has been made in this area, current CF methods still suffer from cold-start and sparsity problems. A primary issue is that the fraction of users willing to rate items tends to be very small in most practical applications, which causes the number of users and/or items with few or no interactions in recommendation databases to be large. As a direct consequence of ratings sparsity, recommender algorithms may provide poor recommendations (reducing accuracy) or decline recommendations (reducing coverage). This paper proposes an ensemble scheme based on a co-training approach, named ECoRec, that drives two or more recommenders to agree with each others’ predictions to generate their own. The experiments on eight real-life public databases show that better accuracy can be obtained when recommender algorithms are simultaneously trained from multiple views and combined into an ensemble to make predictions.

Recommender systems, cultural heritage applications, and the way forward

Abstract Modern recommender systems appeared during the last decade of the 20th century and have already proven their importance in tackling with information overload and content or services personalisation in the modern big-data world. This is markedly suggested by their adoption by major industry parties in a wide variety of domains. The application of recommender systems in the Cultural Heritage domain has appeared relatively recently, particularly during the advent of the 21st century. Nevertheless, a large number of works appeared in the scientific literature, driven primarily from large-scale cultural heritage collaborative projects. This paper presents a review of the current recommender system approaches, including some basic mathematical formalism, and focuses on recommender system applications in Cultural Heritage, to identify the advantages, the benefits and prospects, the disadvantages and pitfalls. The paper discusses the diversity and some common assumptions in the reviewed works, outlines some proposals for future development, and takes a position on the way forward.

A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings

Abstract Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.

Application of Data Mining to E-Commerce Recommendation Systems

The rising power of technology has intensely improved the information storage, collection, and manipulation ability. As the information is growing very rapid along with its complexness, data analysis has become more important. The aim of this paper is to recommend products to the user which are more likely to be purchased. This paper, first describes about different techniques for recommendation and the research regarding recommendation system, then suggests a better approach for a good recommendation system and explains the results of that approach. Here, a combination of k-means clustering algorithm and apriori algorithm on transactional dataset so that a better recommendation list can be obtained.

Hybrid Recommender Systems Process, Challenges, Approaches and Metrics

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Personal price aware multi-seller recommender system: Evidence from eBay

Many e-commerce sites use recommender systems, which suggest products that consumers may want to purchase in order to increase site revenue. Though recommender systems have achieved great success, they have not reached their full potential. Most current systems share a common weakness: they fail to take into account dynamic properties of the offering which could dramatically improve the effectiveness of a recommendation; these characteristics include the product price, promotion indication, and seller's reputation. Particularly, in a multi-seller platform (e.g., eBay, Amazon), where competing firms sell products differentiated mainly by the seller's reputation and product price, modeling consumer's sensitivity to these dynamic properties and incorporating it into a recommender system will optimize sellers’ revenue and market penetration.In this research, we introduce a novel approach for a personal price aware multi-seller recommender system (PMSRS) which implicitly models a consumer's willingness to pay (WTP) for a specific product, taking into account discount indication and seller reputation, and incorporating it within a context-aware recommendation model to improve its effectiveness. We use six months of transactional data from eBay.com to test the proposed approach and prove its validity and effectiveness. Our results show that the proposed approach provides a good estimation of the consumer's WTP, and that incorporating the consumer's WTP and seller's reputation into a recommender system significantly improves its prediction accuracy (F-score improvements of 84% compared to a matrix factorization recommendation model which doesn't take into account the seller's reputation or consumer's WTP).

Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors-based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although social matrix factorization (Social MF) and topic matrix factorization (Topic MF) successfully exploit social relations and item reviews, respectively; both of them ignore some useful information. In this article, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model MR3 to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.

Trust aware recommender system with distrust in different views of trusted users

A recommender system aims to provide users with personalized online product or service recommendations to handle the online information overload problem that keep rapidly increasing. The main problems in the CF recommender system are sparsity and cold start. In order to resolve the problems, one of the current researches has been directed to the CF with a trust aware mechanism that includes trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the performances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the two algorithms.

Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds

Chemically relevant compositions (CRCs) and atomic arrangements of inorganic compounds have been collected as inorganic crystal structure databases. Machine-learning is a unique approach to search for currently unknown CRCs from vast candidates. Herein we propose matrix- and tensor-based recommender system approaches to predict currently unknown CRCs from database entries of CRCs. Firstly, the performance of the recommender system approaches to discover currently unknown CRCs is examined. A Tucker decomposition recommender system shows the best discovery rate of CRCs as the majority of the top 100 recommended ternary and quaternary compositions correspond to CRCs. Secondly, systematic density functional theory (DFT) calculations are performed to investigate the phase stability of the recommended currently unknown CRCs. The phase stability of the 27 compositions reveals that 23 currently unknown compounds are stable. These results indicate that the recommender system has great potential to accelerate the discovery of new compounds.

An Obfuscated Attack Detection Approach for Collaborative Recommender Systems

In the recent times, we have loads and loads of information available over the Internet. It has become very cumbersome to extract relevant information out of this huge amount of available information. So to avoid this problem, recommender systems came into play, which can predict outcomes according to user's interests. Although recommender systems are very effective and useful for users, the most used type of recommender system, i.e. collaborative filtering recommender system, suffers from shilling/profile injection attacks in which fake profiles are inserted into the database in order to bias its output. With this problem in mind,we propose an approach to detect attacks on recommender systems using Random Forest Classifier and find that, when tested at 10% attack, our approach outperformed earlier proposed approaches.

A collaborative approach for research paper recommender system.

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.