Collaborative Filtering Recommendation Research Articles

A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system

Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services.

Collaborative Filtering Recommendation Model Based on Rough User Clustering

Collaborative Filtering Recommendation Model Based on Rough User Clustering

A hybrid recommendation algorithm based on time factor

With the development of social network, helping users find their interesting information become a primary objective for recommend systems. As a most popular recommend algorithm, collaborative filtering recommendation still remains some shortcomings such as data sparseness, cold start and neglecting of variable user interests. With this in mind, a hybrid recommendation algorithm based on time factor is proposed in this paper. A hybrid recommendation model based on time factor aiming to improve the accuracy of user similarity calculations is proposed. This recommendation model includes the user rating, content feature and time factor. Then, the particle swarm optimisation PSO algorithm is exploited to optimise the searching space. The experimental results show that the proposed algorithm can effectively improve accuracy while solving data sparseness and cold start. It can be used in the social network and e-commerce.

Personalized Recommendation Based on Trust and Preference

Collaborative filtering recommendation is a mainstream personalized recommendation method, which has some flaws in actual application. And there are some inaccurate recommendation results in some cases. Considering the relationship of trust and similarity of user preference, this paper introduces trust to recommendation model and considers multi-dimensional factors of user preference, proposes a personalized recommendation method based on trust and preference. The recommendation method can improve the accuracy of recommendation system. Finally, this paper proves effectiveness of this recommendation method through the experiments.

Application and Research on Distributed Collaborative Filtering Recommendation Algorithm Based on Hadoop

Coupled with exponential expansion of the data, efficient computing of existing recommendation algorithm has become an important issue, and the traditional collaborative filtering recommendation algorithm also exist the problem of sparsity. Based on the detailed analysis, the article introduce Hadoop platform into improved collaborative filtering recommendation algorithm, the improved collaborative filtering recommendation algorithm solve the problem of data sparsity, MapReduce parallel computing of recommendation also solve the promble of computational efficiency. In the experiments, the comparative analysis between Hadoop platform implementation and the previous implementation draws the conclusion that the Hadoop platform improves collaborative filtering recommendation algorithm computation efficiently under conditions of large data sets.

Research on Algorithm Recommended by Online Education for Big Data

“Big data” is becoming a hot topic in the Internet. The long tail problem of the massive online courses also becomes the biggest headache for operation team of online education. The manner in which the reader wants most courses show to be presented before the user is the key to improve the quality of online edu-cation. Personalized recommendation system is to discover the readers interests tendency based on the existing user data, project data, and interactive data, thus to provide personalized product recommendation for readers. This article is based on the two kinds of algorithms, namely the content and the collaborative filtering recommendation to propose an improved integration scheme, which can make good use of existing data to discover the useful knowledge for readers’ recommendation. The method firstly solves the sparsity problem in traditional collaborative filtering, and meanwhile we start from the global structure relation of course, to analyze the relationship between the reader and the course more comprehensively. The algorithm to improve the accuracy of recommendation from multiple angles, and provides a feasible method for precise recommendation of online educational video.

A Novel Contextual Information Recommendation Model and Its Application in e-Commerce Customer Satisfaction Management

In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods.

Enhanced Hybrid Recommender System using Social Friend Network

Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

An Electronic Commerce Collaborative Filtering Recommedation Algorithm Based on User Context

With the speedy development of Internet, information technology has provided an unmatched amount of information resources. To help people to find helpful information, electronic commerce personalized recommendation technique emerges. Collaborative filtering is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering recommendation algorithm has the problem of sparsity, which will influence the efficiency of prediction. User context information is rarely considered in the recommendation process, especially in the collaborative filtering. In this paper, a new electronic commerce collaborative filtering recommendation algorithm is given which applies the user context information. This method combines the rating similarity and the user context similarity in the electronic commerce recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity.

A Research of Collaborative Filtering Recommender Method Based on SOM and RBFN Filling Missing Values

A Research of Collaborative Filtering Recommender Method Based on SOM and RBFN Filling Missing Values

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

Neighborhood Evaluation in Recommender Systems Using the Realization Based Entropy Approach

The task of a recommender system evaluation has often been addressed in the literature, however there exists no consensus regarding the best metrics to assess its performance. This research deals with collaborative filtering recommendation systems, and proposes a new approach for evaluating the quality of neighbor selection. It theorizes that good recommendations emerge from good selection of neighbors. Hence, measuring the quality of the neighborhood may be used to predict the recommendation success. Since user neighborhoods in recommender systems are often sparse and differ in their rating range, this paper designs a novel measure to asses a neighborhood quality. First it builds the realization based entropy (RBE), which presents the classical entropy measure from a different angle. Next it modifies the RBE and propose the realization based distance entropy (RBDE), which considers also continuous data. Using the RBDE, it finally develops the consent entropy, which takes into account the absence of rating data. The paper compares the proposed approach with common approaches from the literature, using several recommendation evaluation metrics. It presents offline experiments using the Netflix database. The experimental results confirm that consent entropy performs better than commonly used metrics, particularly with high sparsity neighborhoods. This research is supported by The Israel Science Foundation, Grant #1362/10. This research is supported by NHECD EC, Grant #218639.

An IPTV Recommendation Method Based on Latent Context

In the light of the data differences between network television and the Internet, this paper solve the problem of grading IPTV by the introduction of time context information and computing the latent scores based on the traditional and item-based collaborative filtering recommendation algorithm. Construct the user - item, the item - time model and optimize item similarity calculation so as to ease the difficulty of searching the similar item due to the data scarcity. The experimental results show that the improved method can obviously increase the recommendation precision and has a certain effect on reducing the impact of data scarcity compared with the traditional item-based collaborative filtering.

Clustering- and regression-based multi-criteria collaborative filtering with incremental updates

Recommender systems are a valuable means for online users to find items of interest in situations when there exists a large set of alternatives. Collaborative Filtering (CF) is a popular technique to build such systems which is based on explicit rating feedback on the items by a larger user community. Recent research has demonstrated that the predictive accuracy of CF based recommender systems can be measurably improved when multi-criteria ratings are available, i.e., when users provide ratings for different aspects of the recommendable items. Technically, in particular regression-based techniques have been shown to be a promising means to predict the user’s overall assessment of an item based on the multi-criteria ratings.Since in many domains customer subgroups (segments) exist that share similar preferences regarding the item features, we propose a novel CF recommendation approach in which such customer segments are automatically detected through clustering and preference models are learned for each customer segment. In addition, since in practical application constantly new rating information is available, the proposed method supports incremental updates of the preference models. An empirical evaluation of our method shows that the predictions of the resulting models are more accurate than previous multi-criteria recommendation methods.

Research on Recommendation Algorithm of Matrix Factorization Method Based on MapReduce

Matrix factorization is a collaborative filtering recommendation technique proposed in recent years.In the process of recommendation,each prediction depends on the collaboration of the whole known rating set and the feature matrices need huge storage. So the recommendation with only one node will meet the bottleneck of time and resource. A MapReduce-based matrix factorization recommendation algorithm was proposed to solve this problem.The big feature matrices were shared by Hadoop distributed cache and MapFile techniques.The MapReduce algorithm could also handle multi-λsituation.The experiment on Netflix data set shows that the MapReduce-based algorithm has high speedup and improves the efficiency of collaborative filtering.

Collaborative Filtering Recommendation Technology Based on Genetic Algorithm

-In the current intelligent algorithm recommendation systems, collaborative filtering recommendati

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

소셜 네트워크 분석 기법을 활용한 협업필터링의 특이취향 사용자(Gray Sheep) 문제 해결

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize `degree centrality` in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A `popular item` method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing `Best-N-neighbors` and `Cosine` similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used . Past studies to improve CF performance typically used additional information other than users` evaluations such as demographic data. Some studies applied SNA techniques as a new similarity metric. This study is novel in that it used SNA to separate dataset. This study shows that performance of CF can be improved, without any additional information, when SNA techniques are used as proposed. This study has several theoretical and practical implications. This study empirically shows that the characteristics of dataset can affect the performance of CF recommender systems. This helps researchers understand factors affecting performance of CF. This study also opens a door for future studies in the area of applying SNA to CF to analyze characteristics of dataset. In practice, this study provides guidelines to improve performance of CF recommender systems with a simple modification.

Study on Directed Trust Graph Based Recommendation for E-commerce System

Automated recommender systems have played a more and more important role in marketing and ever increasingly booming e-commerce systems. They provide useful predictions personalized recommendations according to customers’ characteristics and a variety of large and complex product offerings. In many of these recommendation technologies C ollaborative Filtering (CF) has proven to be one of the most successful recommendation method, which has been widely used in many e-commerce systems. The success of CF recommendation depends mainly on locating similar neighbors to get recommendation items. However, many scholars have found that the process of finding similar neighbors often fail, due to some inherent weaknesses of CF based recommendation. In view of this, we propose a trust feedback recommendation algorithm based on directed trust graph (DTG), which is able to propagate trust relationship. In our approach, there is no need to compute similarity between users, but utilize the trust relation between them to conduct prediction calculation. Based on the analysis of human trust perception, we incorporate the process into our recommendation algorithm. Experimental evaluation on r eal life Epinions datasets shows that the effectiveness and practicability of our approach.

Enhancing Collaborative Filtering Recommendation by Utilizing Improved Ant Colony Optimization Algorithm

Collaborative filtering (CF) is the most widely used and successful personalized recommendation technology in web usage mining. The traditional collaborative filtering algorithm based on user static evaluation of the item's neighbour to predict changes of the users’ interests, however, the user’s interest will make a difference over time. Taking the dynamic changes the user’s interest into account in the process, this paper presents a dynamic collaborative filtering recommendation method based on improved ant colony algorithm (EACF). Improved ant colony algorithm takes into account the user access time and access frequency, which can be more representative of the true interests of users. When generating the recommendation, this method not only takes into account the item’s score, but also will take into account intensity of “interest pheromone” on each item. Experimental results show that the EACF can significantly improve the prediction accuracy of the recommendation system compared with traditional CF.