A new similarity measure for collaborative filtering based recommender systems

Internet sources contain a vast amount of information about items that people desire to purchase. It is impossible to evaluate these resources and come to an informed decision. People need automated systems that evaluate previous information and propose item alternatives. Recommending items using a smart system, which is based on the previous user preferences, has growing importance since the available product data is exponentially growing. Additionally, it is difficult to find new and correct things that a user would like among this massive amount of data. To make accurate recommendations with a smart system, researchers and practitioners use collaborative filtering methods with similarity calculation based on user preferences. The crucial point in collaborative filtering is to find a valuable measure that resembles correct similarity between users. The current similarity metrics in the literature have some disadvantages in conducting accurate recommendations. To improve the recommendation performance, this study proposes a novel similarity measure that assesses the distance between the user’s ratings and the median score. Considering distance from the median score is essential since some users may prefer to rate close to the median rather than the extremes. Experiments were conducted with a famous collaborative filtering dataset. Results showed that proposed similarity measure demonstrated superior performance regarding the recommendation accuracy. Implications of our results for XYZ are discussed.

Recommender systems in the e-learning domain assist learners in finding relevant learning materials based on their preferences and goals. One of the main components of such a recommender system is a similarity measurement unit, used to determine the set of learners having the same behavior. Several similarity functions have been proposed in the e-learning domain, with different performances in terms of accuracy and quality of recommendations. Most of these similarity methods do not perform satisfactorily in the presence of cold-start users. In this paper, we present a comparative study of 4 generic similarity measures (Pearson Correlation Similarity, Cosine Vector Similarity, Euclidean Distance Similarity, Jaccard Similarity Correlation) that are widely used in e-learning recommender systems. The evaluation metrics Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the recommender system with the 4 similarity measures. The results indicate better recommendation performance when using Cosine Vector Similarity in cold-start condition.

Recommendation systems play an important role in suggesting relevant information to users. In this paper, we introduce community-wise social interactions as a new dimension for recommendations and present a social recommendation system using collaborative filtering and community detection approaches. We use (i) community detection algorithm to extract friendship relations among users by analyzing user-user social graph and (ii) user-item based collaborative filtering for rating prediction. We developed our approach using map-reduce framework. Our approach improves scalability, coverage and cold start issue of collaborative filtering based recommendation system. We carried out experiments on MovieLens and Facebook datasets, to predict the rating of the movie and produce top-k recommendations for new (cold start) user. The results are compared with traditional collaborative filtering based recommendation system.

A new item similarity based on [formula omitted]-divergence for collaborative filtering in sparse data

Recommendation systems (RS) are very popular for recommending items to its users on internet. Collaborative Filtering (CF) is a technique to recommend items to users on the basis of similarity between the users. Finding similarity between the users is very important challenge for recommendation systems. Two users are considering similar if the similarity between them is greater to a threshold value. The similarity threshold is minimum value of similarity between the users in CF based recommendation system to decide whether two users are considered to be similar or not. In this paper the performance of CF based recommendation system is analyzed on the basis of the similarity threshold. The performance is measured in terms of mean absolute error [MAE] which is calculated on a sample data set. MAE is calculated and compared at different levels of similarity threshold. The proposed work experimentally concludes the implications and benefits of deciding the similarity threshold and concludes that the high value of similarity threshold gives good results.

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologies. In collaborative filtering, similarity calculation is the main issue. In order to improve the accuracy and quality of recommendations, we proposed an improved similarity model, which takes three impact factors of similarity into account to minimize the deviation of similarity calculation. Compared with the traditional similarity measure, the advantages of our proposed model are that it makes full use of rating data and solves the problem of co-rated items. To validate the efficiency of the proposed algorithm, experiments were performed on four datasets. Results show that the proposed method can effectively improve the preferences of the recommender system and it is suitable for the sparsity data.

Evaluation Of Recommendation System On A Movie Dataset

Presenting small chunks of interesting information to a target user from the large pool of information available on World Wide Web are the primary task of a recommender system. Memory based Collaborative Filtering generates recommendations based on preferences of those users whose past preferences are similar to the current preferences of the target user. These users collectively form the Nearest Neighbor Set (NN Set) of the target user. The better the selection of NN Set, the better is the generation of recommendations. In the proposed scheme, the user ratings (preferences) on the available items are divided into two levels. Level I consist of ratings on popular items and Level II consist of ratings on unpopular items. The proposed similarity measure, “TSimD(U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X1</sub> ,U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X2</sub> )”, between two users is based on weighted difference entropy. Modified memory based Collaborative Filtering calculates the proposed similarity measure at both the levels to improve the selection of users in the NN Set of the target user. It selects those users in the NN Set who are more similar to the target user with respect to items at Level II as compared to similarity between them with respect to items at Level I. The results on Movie Lens dataset depicted that the proposed similarity measure had higher accuracy than the Weighted Difference Entropy based similarity measure which worked on the entire preferences of the users. The proposed similarity measure was also compared with other similarity measures (like Cosine, Pearson Correlation, Spearman and Rating Frequency based Similarity Measure) which were also obtained at two levels.

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.

The neighbor selection that determines which users are exploited to estimate a target user's ratings has an important influence on the accuracy of recommendations of collaborative filtering based recommender system. Two kinds of ways for neighbor selection: KNN and cluster-based, are lack of specificity which refers to selecting different appropriate neighbors for different given target users, and thus limit the accuracy of recommendation. Therefore, in this paper, firstly, we propose a method that employs the evolutionary algorithm to optimize neighbors for all target users. Secondly, overcoming the high time complexity of the first one, we present another approach in which community detection algorithm is utilized as a preprocessing, and then the evolution algorithm is employed to optimize the neighborhood size for every community. We present experiments on a standard benchmark data-set, and the results show that the two methods both realize the specificity in neighbor selection, and accordingly lead to a higher accuracy of recommendations. Besides, the second one makes a good compromise between the specificity and time complexity.

Memory-based collaborative filtering (CF) techniques have been widely implemented for predicting ratings to unseen items by aggregating ratings of similar users or items in recommender systems (RS). Usually, sufficient ratings from similar users or similar items are not available in the rating matrix, due to the data sparsity problem. Further, these techniques suffer from correlation based problems inherent in used similarity measures. Consequently, higher prediction accuracy cannot be achieved. In this paper, we propose the use of fuzzy Naive Bayesian (FNB) classifier for user based CF and item based CF for implicitly computing similarity between users as well as items on the basis of conditional probabilities and develop fuzzy Naive Bayesian classifier to user based CF (FNB-UB-CF) and item based CF (FNB-IB-CF). We further develop a hybrid RS (FNB-UB-IB-CF) by combining the proposed FNB-UB-CF and FNB-IB-CF. Their combinations would be helpful in alleviating the sparsity because both user ratings and item ratings are employed. Experimental results demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendation quality using a popular MovieLens dataset.

The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

Interval-valued intuitionistic fuzzy environment is appropriate for most of the practical scenarios involving uncertainty, vagueness, and insufficient information. Entropy, similarity, distance, inclusion, and cross entropy measures are a few methods used for measuring uncertainty and classifying fuzzy sets and its generalizations. Entropy of a fuzzy set describes fuzziness degree of the set and similarity measure measures similarity between two fuzzy or members of its extended family. This paper presents generalized entropy and similarity measures for interval-valued intuitionistic fuzzy sets. Further, the proposed similarity measure is compared with some existing measure of similarity with the help of an illustrative example, and a method is used to define optimal point using the existing information. Finally, entropy and similarity measures are used to identify best alternatives to solve multi-attribute decision making.

Nearest biclusters collaborative filtering framework with fusion