Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem

Collaborative filtering (CF), the most successful and widely used technique, recommends items based on the preferences of similar users. The main potentials of CF are its cross-genre recommendation ability, and that it is completely independent of representation of the items being recommended. However, CF suffers from sparsity and cold start problems. On the other hand, a highly effective variant of content-based filtering (CBF), reclusive methods (RMs) based on the preference of the single individual for whom recommendations to be made, provides a methodology that considers uncertainty and the multivalued nature of item features as well as user preferences in a content-based framework using fuzzy logic approaches. The adoption of RM paradigm has several advantages when compared to CF such as sparsity and new item problem, but it suffers from overspecialization and limited content analysis. In view of the complementary nature of CF and RM, we develop a hybrid recommender system (RS) that helps in alleviating aforementioned problems in each approach. First, we propose fuzzy naïve Bayesian classifier based CF (FNB-CF) and RM (FNB-RM) for handling correlation-based similarity problems. To overcome individual weaknesses of FNB-CF and FNB-RM, we develop a hybrid RS, FNB-CF-RM. Effectiveness of our proposed hybrid RS is demonstrated through experimental results using the MovieLens and IMDb data sets.

Recommender systems are essential to overcome the information overload problem in professional learning environments. In this paper, we investigate interest-based recommendation in academic networks using social network analytics (SNA) methods. We implemented 21 different recommendation approaches based on traditional Collaborative Filtering (CF), Singular value Decomposition (SVD)-based RS, Trust-based CF, and SNA-based techniques for recommending new collaborators and research topics to the researchers. The evaluation results show that SNA-based recommendation outperforms traditional CF methods in terms of coverage and thus can provide an effective solution to the sparsity and cold start problems in recommender systems.

Using community preference for overcoming sparsity and cold-start problems in collaborative filtering system offering soft ratings

Recommender Systems are used to mitigate the information overload problem in different domains by providing personalized recommendations for particular users based on their implicit and explicit preferences. However, Item-based Collaborative Filtering (CF) techniques, as the most popular techniques of recommender systems, suffer from sparsity and new item limitations which result in producing inaccurate recommendations. The use of items’ semantic information besides the inclusion of multi-criteria ratings can successfully alleviate such problems and generate more accurate recommendations. This paper proposes an Item-based Multi-Criteria Collaborative Filtering algorithm that integrates the items’ semantic information and multi-criteria ratings of items to lessen known limitations of the item-based CF techniques. According to the experimental results, the proposed algorithm prove to be very effective in terms of dealing with both of the sparsity and new item problems and therefore produce more accurate recommendations when compared to standard item-based CF techniques.

Evaluation Of Recommendation System On A Movie Dataset

An explicit trust and distrust clustering based collaborative filtering recommendation approach

Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system. In this paper, we propose a hybrid collaborative filtering model based on a Makovian random walk to address the data sparsity and cold start problems in recommendation systems. More precisely, we construct a directed graph whose nodes consist of items and users, together with item content, user profile and social network information. We incorporate user's ratings into edge settings in the graph model. The model provides personalized recommendations and predictions to individuals and groups. The proposed algorithms are evaluated on MovieLens and Epinions datasets. Experimental results show that the proposed methods perform well compared with other graph-based methods, especially in the cold start case.

Recommender Systems is a special type of information filtering system which has become important in the information overloaded and strategic decision making environment. Recommender System is used to produce meaningful suggestions about new items for particular consumers. These recommendations may be based on the user profile or item ratings, facilitate the users to make decisions in multiple contexts, such as what items to buy, what online news to read or what music to listen. Recommender Systems helps their founders to increase profits by recommending items and attracting new consumers. Collaborative filtering technique recommends items basis of conclusion of opinions about various products by users of similar profile to the active user. This technique requires user-items-ratings matrix. Although this is the most mature and commonly implemented technique, it faces major problem of Data Sparsity problem. Sparsity Problem occurs as a result of lack of enough information when only a few of the total number of items are rated by the users. This produces a sparse user item matrix leads to weak recommendations. This paper presents a recommender system using collaborative filtering implemented with RapidMiner tool. The proposed recommendation system is designed with users’ similarity calculated by Sequence and Set Similarity Measure (S3M) with utilizing similarity upper approximation and a Singular Value Decomposition (SVD) model based technique used for recommending ratings for removing sparsity.

This paper proposes the association word mining method. Using this method, the profile of the collaborative user is created, and based on this profile, users are grouped according to the vector space model and Kmeans algorithm. Consequently, the existing collaborative filtering system’s problems of sparsity and of recommendations based on the degree of correlation of user preferences are eliminated. Moreover, to address the said system’s shortcoming whereby items are recommended according to the degree of correlation of the two most similar users within a group, entropy is used. Thus, the typical preference of the group is extracted. Since user preferences cannot be automatically regarded as accurate data, users within the group who have entropies beyond the threshold are selected as typical users. The typical preference can be extracted by assigning typical user preferences in the form of weights. The method enables dynamic recommendation because it decreases the inaccuracy of recommendations based on unproven user preferences.

Recommender systems are powerful tools that help users to deal with information overload problem. Collaborative Filtering (CF) approach has been widely used to build recommender systems over the past decades. However, the performance of CF is limited by sparsity and cold start problems, which are very common in real world situations. In recent years, many review-based approaches have been developed to integrate textual reviews into recommendation process, since they provide much more information about item/user profiles than ratings. The use of text analysis and opinion mining methods helps extracting such information. In this paper, we first introduce standard CF techniques and their main challenges. Then, we present different kind of information that can be extracted from user reviews. After that, we describe recent works that exploit review elements to improve the CF-based recommendations. Finally, we discuss their practical implications.

Background: Due to enormous data on web sites, recommending users for every item is impossible. For this problem, Recommender Systems (RS) are introduced. RS is categorized into Content-Based (CB), Collaborative Filtering (CF) and Hybrid RS. Based on these techniques, recommendations are made to the user. In this, CF is the recent technique used in RS in which tagging features also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues, here we have proposed Tag and Time weight model with GA in Collaborative Tagging. Methods: Here we have proposed a method Collaborative Tagging (CT) with Tag and Time weight model with real value genetic algorithm which enhances the recommendation quality by removing the issues of sparsity and cold start user problems with the help of missing value prediction. Here in this system, the sparsity problem can be removed using missing value prediction and cold start problems are removed using tag and time weight model using GA. Results: In this study, we have compared the results of Collaborative Filtering with Cosine Similarity (CF-CS), Collaborative Filtering with Diffusion Similarity (CF-DS), Tag and Time Weight Model with Diffusion Similarity (TAW-TIW-DS) and Tag and Time Weight Model Using Diffusion Similarity and Genetic Algorithm (TAW-TIW-DS-GA). Conclusion: Here we have compared the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Hit-rate and Hit-rank. Based on these metrics for every split, TAW-TIW-DS-GA showed best results as compared to the existing approach.

The popularity of movies has increased in recent years. There are thousands of films produced each year. These films make it challenging for movie lovers to pick the ideal film to see. We propose a recommendation system that strives to offer guidance in selecting films. Depending on the method employed, recommendation systems can be categorized into three groups: collaborative filtering, content-based filtering, and hybrid filtering. In this work, collaborative filtering, one of the methods frequently used in recommendation systems was used. There are two ways to the Collaborative Filtering approach: User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF). There are two methods for finding similar items or users: Cosine and Pearson similarities. The Cosine similarity approach is one way to determine how similar two items are. Additionally, the Pearson Correlation Coefficient approach, which determines similarities between objects by calculating linear correlations between two sets, is the most widely employed. This study aims to determine which system produces the highest item similarity in IBCF and predicted ratings to actual ratings using 90% training and 10% testing data. The data set taken from MovieLens.org consists of 943 users from 1664 movies with 99392 ratings. The MovieLens data collection will be analyzed with the RStudio and the R package recommenderlab. The results reveal that the IBCF with Cosine similarities shows the number of items recommended n top-rated movies to each user for 10 movies. The IBCF can identify the most recommended films and creates a frequency distribution of items.

Collaborative filtering has been very successful in both research and E-commence applications. One of the most popular collaborative filtering algorithms is the k-Nearest Neighbor (KNN) method, which finds k nearest neighbors for a given user to predict his interests. Previous research on KNN algorithm usually suffers from the data sparseness problem, because the quantity of items users voted is really small. The problem is more severe in web-based applications. Cluster-based collaborative filtering has been proposed to solve the sparseness problem by averaging the opinions of the similar users. However, it does not bring consistent improvement on the performance of collaborative filtering since it produces less-personal prediction. In this paper, we propose a clustering-based KNN method, which combines the iterative clustering algorithm and the KNN to improve the performance of collaborative filtering. Using the iterative clustering approach, the sparseness problem could be solved by fully exploiting the voting information first. Then, as a smoothing method to the KNN method, cluster-based KNN is used to optimize the performance of collaborative filtering. The experimental results show that our proposed cluster-based KNN method can perform consistently better than the traditional KNN method and clustering-based method in large-scale data sets.

With the speedy development of network, information technology has provided an unmatched amount of information resources. It has also led to the problem of information overload. However, people experiences and knowledge often do not enough to process the vast amount of usable information. Thus, approaches to help find resources of interest have attracted much attention from researchers. And recommender systems have arrived to solve this problem. Recommender system plays an important role mainly in an electronic commerce environment as a new marketing strategy. Although a varied of recommendation techniques has been developed recently, collaborative filtering has been known to be the most successful recommendation techniques and has been used in a number of different applications. But traditional collaborative filtering recommendation algorithm has the problem of sparsity. Aiming at the problem of data sparsity for personalized filtering systems, a collaborative filtering recommendation algorithm based on user rating similarity and user attribute similarity is given. This approach not only considers the user item rating information, but also takes into account the user attribute.

With the development of e-commerce, shopping on-line is becoming more and more popular. When we need to decide whether to purchase a product or not on line, the opinions of others become important. The convenience of new web technologies enables us to freely express our opinions and reviews for various products we have purchased which leads to a serious problem, information overloading. How to mine these review data to understand customers' preferences and make recommendations is crucial to merchants and researchers. Traditional collaborative filtering (CF) algorithm is one of the most successful recommendation system technologies. The core idea of CF algorithm is to recommend products based on other people who have similar tastes with target users. However, the ability of CF is limited by the sparsity problem, which is very common in reality. The reason derives from the fact that traditional CF method only takes users' ratings into account. In this paper, we propose a new personalized recommendation model, i.e. topic model based collaborative filtering (TMCF) utilizing users' reviews and ratings. We exploit extended LDA model to generate topic allocations for each review and then obtain each user's preference. Moreover, a new metric is designed to measure similarity between users alleviating the sparsity problem to a large extent. Finally, recommendations are made based on similar users' ratings. Experiments on seven data sets indicate better prediction accuracy than other traditional and state-of-the-art methods with substantial improvement in alleviating the sparsity problem.