Collaborative Filtering Approach Research Articles

A Rating-Based Integrated Recommendation Framework with Improved Collaborative Filtering Approaches

Collaborative filtering (CF) approach is successfully applied in the rating prediction of personal recommendation. But individual information source is leveraged in many of them, i.e., the information derived from single perspective is used in the user-item matrix for recommendation, such as user-based CF method mainly utilizing the information of user view, item-based CF method mainly exploiting the information of item view. In this paper, in order to take full advantage of multiple information sources embedded in user-item rating matrix, we proposed a rating-based integrated recommendation framework of CF approaches to improve the rating prediction accuracy. Firstly, as for the sparsity of the conventional item-based CF method, we improved it by fusing the inner similarity and outer similarity based on the local sparsity factor. Meanwhile, we also proposed the improved user-based CF method in line with the user-item-interest model (UIIM) by preliminary rating. Second, we put forward a background method called user-item-based improved CF (UIBCF-I), which utilizes the information source of both similar items and similar users, to smooth itembased and user-based CF methods. Lastly, we leveraged the three information sources and fused their corresponding ratings into an Integrated CF model (INTE-CF). Experiments demonstrate that the proposed rating-based INTE-CF indeed improves the prediction accuracy and has strong robustness and low sensitivity to sparsity of dataset by comparisons to other mainstream CF approaches.

A comparison among approaches for recommending learning objects through collaborative filtering algorithms

PurposeThe purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration.Design/methodology/approachThe authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage.FindingsClustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be.Research limitationsThe methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions.Originality/valueThis research provides evidence toward new recommendation methods directed toward LO repositories.

Identifying new business opportunities from competitor intelligence: An integrated use of patent and trademark databases

This study aims to analyze the position of technology-centered companies in complex market dynamics and discover new business opportunities from competitor intelligence. For this, we consider both technology and market characteristics in providing competitor intelligence by utilizing patent data as a representative proxy for a firm's technology, and trademark data as an information source for the firm's target goods and services. To analyze the two types of data, a collaborative filtering approach together with portfolio analyses and association mining techniques were adopted. Theoretically, this is one of the earliest attempts to combine patent data and trademark data to investigate corporate strategies. In practice, the research results are expected to be used as a decision criterion to diagnose the economic value that companies can obtain by entering the market, as well as the technological value to be passed onto their customers. Thus, the proposed approach can be useful to support effective technology and business strategies in a firm.

Social regularized von Mises–Fisher mixture model for item recommendation

Collaborative filtering (CF) is a widely used technique to guide the users of web applications towards items that might interest them. CF approaches are severely challenged by the characteristics of user-item preference matrices, which are often high dimensional and extremely sparse. Recently, several works have shown that incorporating information from social networks—such as friendship and trust relationships—into traditional CF alleviates the sparsity related issues and yields a better recommendation quality, in most cases. More interestingly, even with comparable performances, social-based CF is more beneficial than traditional CF; the former makes it possible to provide recommendations for cold start users. In this paper, we propose a novel model that leverages information from social networks to improve recommendations. While existing social CF models are based on popular modelling assumptions such as Gaussian or Multinomial, our model builds on the von Mises–Fisher assumption which turns out to be more adequate, than the aforementioned assumptions, for high dimensional sparse data. Setting the estimate of the model parameters under the maximum likelihood approach, we derive a scalable learning algorithm for analyzing data with our model. Empirical results on several real-world datasets provide strong support for the advantages of the proposed model.

Towards a knowledge-based probabilistic and context-aware social recommender system

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).

Human-Aware Plan Recognition

Plan recognition aims to recognize target plans given observed actions with history plan libraries ordomain models in hand. Despite of the success of previous plan recognition approaches, they all neglect the impact of human preferences on plans. For example, a kid in a shopping mall might prefer to "executing'' a plan of playing in water park, while an adult might prefer to "executing'' a plan of having a cup of coffee. It could be helpful for improving the plan recognition accuracy to consider human preferences on plans. We assume there are historical rating scores on a subset of plans given by humans, and action sequences observed on humans. We estimate unknown rating scores based on rating scores in hand using an off-the-shelf collaborative filtering approach. We then discover plans to best explain the estimated rating scores and observed actions using a skip-gram based approach. In the experiment, we evaluate our approach in three planning domains to demonstrate its effectiveness.

User based Recommender Systems using Implicative Rating Measure

This paper proposes the implicative rating measure developed on the typicality measure. The paper also proposes a new recommendation model presenting the top N items to the active users. The proposed model is based on the user-based collaborative filtering approach using the implicative intensity measure to find the nearest neighbors of the active users, and the proposed measure to predict users’ ratings for items. The model is evaluated on two datasets MovieLens and CourseRegistration, and compared to some existing models such as: the item based collaborative filtering model using the Jaccard measure, the user based collaborative filtering model using Jaccard measure, the popular items based model, the latent factor based model, and the association rule based model using the confidence measure. The experimental results show that the performance of the proposed model is better when compared to other five models.

INTERACTION COMMERCE, A TECHNOLOGICAL ARCHITECTURE FOCUSED ON RECOMMENDER SYSTEM

This paper aims at introducing a type of social commerce architecture to which the name Commerce has been given. First, a global description of the main macro-components forming the structure of this architecture is provided. Such components also take care of managing e-commerce activities and social relationships within the architecture. Second, the focus is set on the analysis of the single components that are key to the social aspects of the architecture. A special chapter is then entirely focussed on a topic that is considered extremely important by the entire research community, i.e., recommender systems. After providing a general introduction on the topic, the two most common recommendation approaches are analyzed and compared. These are the content-based approach and the collaborative filtering approach. The analysis has shown how all recommender systems are threatened by the cold-start problem. Studying recommender systems has allowed for their implementation in the architecture, which now has a new “social” approach that is able to solve the new user cold-start problem. An architecture prototype was developed and tested in order to be validated. To cite this document: Luca Salvatori and Fausto Marcantoni, Interaction commerce, a technological architecture focused on recommender system, International Journal of Electronic Commerce Studies, Vol.7, No.2, pp.105-134, 2016. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1443

Enhanced similarity measure for personalized cloud services recommendation

SummaryCloud users are overwhelmed with great numbers of cloud services. Service recommender systems evaluate the services that provide same functionalities according to the user requirements. A key enabler to accurate recommendation in recommender systems is the appropriate determination of similar users. This paper contributes to the personalized cloud services recommendation area. In specific, we introduce a user‐based similarity measure that integrates relevant similarity aspects: user demographic information, service ratings, and user interest. The proposed similarity measure is used in a hybrid collaborative filtering (CF) approach that leverages the advantages of both model‐ and memory‐based approaches to improve the recommendation process. Experimental evaluation on real‐world services data set shows that the proposed approach outperforms other CF approaches in respect of the prediction accuracy and recommendation time while maintaining better or same coverage.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Finding users preferences from large-scale online reviews for personalized recommendation

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers' opinions on products or services, which offer valuable information about users' preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users' individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.

RLCF: A collaborative filtering approach based on reinforcement learning with sequential ratings

AbstractWe present a novel approach for collaborative filtering, RLCF, that considers the dynamics of user ratings. RLCF is based on reinforcement learning applied to the sequence of ratings. First, we formalize the collaborative filtering problem as a Markov Decision Process. Then, we learn the connection between the temporal sequences of user ratings using Q-learning. Experiments demonstrate the feasibility of our approach and a tight relationship between the past and the current ratings. We also suggest an ensemble learning in RLCF and demonstrate its improved performance.

A reinforced collaborative filtering approach based on similarity propagation and score predication graph

In the era of big data, the rapid development of mobile participatory sensing devices brings the explosive expansion of data, making information overload a serious problem. In this case, a personalized recommendation system on mobile social media appears. Collaborative filtering is the most widely used approach in a recommendation system. Nevertheless, there still exist many problems, such as the serious data sparsity problem and the cold start problem. Existing approaches cannot effectively solve these problems. Most of the existing recommendation approaches are based on single information source and cannot effectively solve the cold start and data sparsity problems. In addition, some approaches proposed to solve data sparsity fail to consider the effects of users’ influences and prediction order on recommendation accuracy. Accordingly, from the perspective of increasing the categories of information, the similarity propagation approach based on a heterogeneous network is proposed to ease the cold start problems by improving the similarity calculation method. In addition, to ease the data sparsity problems, we propose a hybrid collaborative filtering approach based on a score prediction graph to finish the user-item score matrix in order. Finally, we conduct validation experiments on the MovieLens dataset. Compared with five state-of-the-art approaches, our approach outperforms them in terms of the performances of mean absolute error, root-mean-square error, recall, and diversity.

A SEMANTIC-BASED COLLABORATIVE FILTERING FOR RECOMMENDATION SYSTEMS

In the present days the web domain is improved with new types of services, with the increase in service and cloud computing. As a result new forms of web content collecting/designing is done based on the numerous openly available web services online. These services are utilized in many ways by different domains and with the exponential growth of these web services users are experiencing difficulties in finding and utilizing a best matching service for their mashup. A collaborative filtering approach is going to filter and recognize the similar services under same cluster and followed by those evaluation recommendations are made. Semantic based collaborative filtering for recommendation system (SCF-RS) is proposed using clustering and to improve the accuracy of the system.

Review of Social Collaborative Filtering Recommender System’s Methods

Recommender Systems plays a vital role in e-commerce. The goal of recommender system is to present the user with the personalized information that matches with the user’s interest. Now a days, user’s interest is leaning towards social networks. Social Networking Sites provide users a platform to connect and share their information with other users who share similar interests with user. The popularity of social networking sites is increasing day by day. Recommender systems are now using the social information for their analysis and prediction process. Collaborative filtering approach is assumed to be the broadly approved technique of recommender system. Collaborative filtering method recommends an item to a user based on the preferences of other users who share analogous interest with the active user. In this paper, we have presented a study of collaborative filtering based social recommender system.

Diversifying web service recommendation results via exploring service usage history

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users’ potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.

Recommendation engine based on derived wisdom for more similar item neighbors

Collaborative filtering (CF) is a popular and widely accepted recommendation technique. CF is an automated form of word-of-mouth communication between like-minded or similar users. The search for these similar users as neighbors from a large user population challenges the scalability of the user based CF approach. As a remedy, an item based CF, pre-computes pairwise item similarities to identify item neighbors. However, data sparsity remains here a major concern, as most of the neighbors of the given item might not be rated by the active user. Consequently, in the traditional item based CF approach, the neighborhood comprises of distant item neighbors having relatively low similarities which in turn affects the overall recommendation quality. The current work addresses this shortcoming in the existing item based CF approach. As a solution, we propose a hybrid user---item based CF where the item neighbors having highest similarity with the given item are selected, irrespective of whether they are rated by the active user. Subsequently, to handle sparsity, missing ratings for some of these selected item neighbors are imputed by multiple linear or ordinal logistic regression. In this approach, ratings of the active user are regressed with ratings of their most similar user(s). The motivation behind this work is to rely on closer rather than distant neighbors, which despite their presence were not used for generating recommendations in the past. The efficacy of the proposed hybrid approach utilizing both user and item similarities is established by its superior predictive performance over three different datasets.

Reputation Transferability in Online Labor Markets

Online workplaces such as oDesk, Amazon Mechanical Turk, and TaskRabbit have been growing in importance over the last few years. In such markets, employers post tasks on which remote contractors work and deliver the product of their work online. As in most online marketplaces, reputation mechanisms play a very important role in facilitating transactions, since they instill trust and are often predictive of the employer’s future satisfaction. However, labor markets are usually highly heterogeneous in terms of available task categories; in such scenarios, past performance may not be an accurate signal of future performance. To account for this natural heterogeneity, in this work, we build models that predict the performance of a worker based on prior, category-specific feedback. Our models assume that each worker has a category-specific quality, which is latent and not directly observable; what is observable, though, is the set of feedback ratings of the worker and of other contractors with similar work histories. Based on this information, we provide a series of models of increasing complexity that successfully estimate the worker’s quality. We start by building a binomial model and a multinomial model under the implicit assumption that the latent qualities of the workers are static. Next, we remove this assumption, and we build linear dynamic systems that capture the evolution of these latent qualities over time. We evaluate our models on a large corpus of over a million transactions (completed tasks) from oDesk, an online labor market with hundreds of millions of dollars in transaction volume. Our results show an improved accuracy of up to 25% compared to feedback baselines and significant improvement over the commonly used collaborative filtering approach. Our study clearly illustrates that reputation systems should present different reputation scores, depending on the context in which the worker has been previously evaluated and the job for which the worker is applying. This paper was accepted by Lorin Hitt, information systems.

A New Improved Clustering Algorithm based Diversified Web Page Recommendation

tremendous growth of internet over the years, has given rise to the large number of web services, containing lot of information. Due to this information overload, it has become difficult to get the correct information. Web Service Recommendation system focuses on satisfying the user's potential interests. Most of the existing recommendation approaches focus only on missing QoS values only, assuming that the result contains independent web services, which might not be true. As a result redundant web services appear in the list. The existing system takes into consideration active user's QoS preferences as well as diversification of the web services list. First, the active user's usage history is mined, and then the experiences of other service users are collected through collaborative filtering approach. Scores are computed for the web service candidates by measuring their relevance with historical and potential user interest and the QoS utility. Web Service graph is constructed based on the functional similarity of the web service candidates. Finally, the diversity- aware web service ranking algorithm is applied on the web service candidates based on the scores calculated and the diversified degree derived from the web service graph. Keywordsservice recommendation, diversity, user interest,

A Recommendation System Based on Regression Model of Three-Tier Network Architecture

The sparsity problem of user-item matrix is a major obstacle to improve the accuracy of the traditional collaborative filtering systems, and, meanwhile, it is also responsible for cold-start problem in the collaborative filtering approaches. In this paper, a three-tier network Architecture, which includes user relationship network, item similarity network, and user-item relationship network, is constructed using comprehensive data among the user-item matrix and the social networks. Based on this framework, a Regression Model Recommendation Approach (RMRA) is established to calculate the correlation score between the test user and test item. The correlation score is used to predict the test user preference for the test item. The RMRA mines the potential information among both social networks and user-item matrix to improve the recommendation accuracy and ease the cold-start problem. We conduct experiment based on KDD 2012 real data set. The result indicates that our algorithm performs superiorly compared to traditional collaborative filtering algorithm.