18,762 research outputs found
Improved SVD + + Recommendation Algorithm Based on Fusion Time Factor
Collaborative filtering algorithm is widely used in recommendation system. Aiming at the problems of data sparsity and low recommendation accuracy in traditional collaborative filtering algorithm, an improved recommendation algorithm is proposed PT _ SVD++. Firstly, the attribute information of users and the implicit feedback information of items are introduced to improve the SVD++ algorithm, which solves the insufficient utilization of information and alleviates the problem of sparse data;Secondly the time effect model is established to further improve the accuracy of the prediction results. The experimental results on MovieLens dataset show that compared with other algorithms, the average absolute error and root mean square error of this algorithm are lower, and its recommendation accuracy is higher
Degree correlation effect of bipartite network on personalized recommendation
In this paper, by introducing a new user similarity index base on the
diffusion process, we propose a modified collaborative filtering (MCF)
algorithm, which has remarkably higher accuracy than the standard collaborative
filtering. In the proposed algorithm, the degree correlation between users and
objects is taken into account and embedded into the similarity index by a
tunable parameter. The numerical simulation on a benchmark data set shows that
the algorithmic accuracy of the MCF, measured by the average ranking score, is
further improved by 18.19% in the optimal case. In addition, two significant
criteria of algorithmic performance, diversity and popularity, are also taken
into account. Numerical results show that the presented algorithm can provide
more diverse and less popular recommendations, for example, when the
recommendation list contains 10 objects, the diversity, measured by the hamming
distance, is improved by 21.90%.Comment: 9 pages, 3 figure
A New Recommendation Algorithm Based on User’s Dynamic Information in Complex Social Network
The development of recommendation system comes with the research of data sparsity, cold start, scalability, and privacy protection problems. Even though many papers proposed different improved recommendation algorithms to solve those problems, there is still plenty of room for improvement. In the complex social network, we can take full advantage of dynamic information such as user’s hobby, social relationship, and historical log to improve the performance of recommendation system. In this paper, we proposed a new recommendation algorithm which is based on social user’s dynamic information to solve the cold start problem of traditional collaborative filtering algorithm and also considered the dynamic factors. The algorithm takes user’s response information, dynamic interest, and the classic similar measurement of collaborative filtering algorithm into account. Then, we compared the new proposed recommendation algorithm with the traditional user based collaborative filtering algorithm and also presented some of the findings from experiment. The results of experiment demonstrate that the new proposed algorithm has a better recommended performance than the collaborative filtering algorithm in cold start scenario
Expert Mining Collaborative Filtering Recommendation Algorithm Based on Signal Fluctuation
This paper proposes an advanced expert collaborative filtering recommendation algorithm. Although ordinary expert system filtering algorithms have improved the recommendation accuracy of collaborative filtering technology to a certain extent, they have not screened the level of expertise of experts, and the credibility of experts varies. Therefore, this paper proposes an expert mining system based on signal fluctuations. The algorithm uses signal processing technology to filter the level of experts. This method introduces a kurtosis factor. Regarding the user's rating sequence as a random discrete signal, and then randomly sorting the user's ratings k times, the average kurtosis of the user is obtained. And take the kurtosis value as the credibility of expert users. Through experiments on multiple datasets including MovieLens, Jester, Booking-Crossings, and Last.fm, we have proved the advancement and reliability of our method
Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items
The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions
The improvement of Item-based collaborative filtering algorithm in recommendation system using similarity index
The extensive and increase use of high-tech product in business has generated a huge amount of business information to be processed in many fields. Thus, a recommendation system is introduced as an effective strategy to manage the business information overload problem. The system aims to filters enormous information and proposes appropriate suggestions to users. A collaborative filtering algorithm is one of the algorithms applied in the recommendation system. However, the collaborative filtering algorithm faces cold-start problem, where new items in the shopping list are not identified and recognized by the system. Hence, this study proposes an improved collaborative filtering algorithm which aims to alleviate the cold-start problem by combining the item rating and item attributes in similarity index. The performance of enhanced algorithm was compared to existing collaborative filtering algorithms in term of precision rate, recall rate and F1 score using Movielens dataset. The algorithm’s efficiency, objectiveness, and accurateness towards its performances were measured. Finally, the experimental results showed that the proposed algorithm get 15 percent precision rate, 6 percent recall rate and 9 percent F1 score. Thus, it proved to be more effective in deal with cold-start problems by using new similarity index, and also can make recommendations on new items in different fields with satisfactory accuracy for better recommendation result. Theoretically, this study contributes to improve the collaborative filtering algorithm in recommendation system for overcome the cold-start problem by analyzing more item attributes to extract more information to the algorithm. Besides, the proposed algorithms can be applied in many fields for cold-items recommendation and to enhance the quality of the recommendation system
A personalized and context-aware news offer for mobile devices
For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer
Discovering E-commerce Sequential Data Sets and Sequential Patterns for Recommendation
In E-commerce recommendation system accuracy will be improved if more complex sequential patterns of user purchase behavior are learned and included in its user-item matrix input, to make it more informative before collaborative filtering. Existing recommendation systems that use mining techniques with some sequences are those referred to as LiuRec09, ChoiRec12, SuChenRec15, and HPCRec18. LiuRec09 system clusters users with similar clickstream sequence data, then uses association rule mining and segmentation based collaborative filtering to select Top-N neighbors from the cluster to which a target user belongs. ChoiRec12 derives a user’s rating for an item as the percentage of the user’s total number of purchases the user’s item purchase constitutes. SuChenRec15 system is based on clickstream sequence similarity using frequency of purchases of items, duration of time spent and clickstream path. HPCRec18 used historical item purchase frequency, consequential bond between clicks and purchases of items to enrich the user-item matrix qualitatively and quantitatively. None of these systems integrates sequential patterns of customer clicks or purchases to capture more complex sequential purchase behavior. This thesis proposes an algorithm called HSPRec (Historical Sequential Pattern Recommendation System), which first generates an E-Commerce sequential database from historical purchase data using another new algorithm SHOD (Sequential Historical Periodic Database Generation). Then, thesis mines frequent sequential purchase patterns before using these mined sequential patterns with consequential bonds between clicks and purchases to (i) improve the user-item matrix quantitatively, (ii) used historical purchase frequencies to further enrich ratings qualitatively. Thirdly, the improved matrix is used as input to collaborative filtering algorithm for better recommendations. Experimental results with mean absolute error, precision and recall show that the proposed sequential pattern mining-based recommendation system, HSPRec provides more accurate recommendations than the tested existing systems
iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering
The growth of Internet commerce has stimulated the use of collaborative
filtering (CF) algorithms as recommender systems. A collaborative filtering
(CF) algorithm recommends items of interest to the target user by leveraging
the votes given by other similar users. In a standard CF framework, it is
assumed that the credibility of every voting user is exactly the same with
respect to the target user. This assumption is not satisfied and thus may lead
to misleading recommendations in many practical applications. A natural
countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take
account of the difference in the credibilities of the voting users when
performing CF. To this end, this paper presents a trust inference approach,
which can predict the implicit trust of the target user on every voting user
from a sparse explicit trust matrix. Then an improved CF algorithm termed
iTrace is proposed, which takes advantage of both the explicit and the
predicted implicit trust to provide recommendations with the CF framework. An
empirical evaluation on a public dataset demonstrates that the proposed
algorithm provides a significant improvement in recommendation quality in terms
of mean absolute error (MAE).Comment: 6 pages, 4 figures, 1 tabl
A preprocessing method for improving effectiveness of Collaborative Filtering
Collaborative filtering uses information about customers’ preferences to make personal product recommendations and is achieving widespread success in e-Commerce. However, the traditional collaborative filtering algorithms do not response accurately to customers’ needs. The quality of the recommendation needs to be improved in order to support personalized service to each customer. In this paper, we present novel method to improve the accuracy of the collaborative filtering algorithm. We borrow vector space model from information retrieval theory and use it to effectively discriminate the preference weights on the items for each customer. The proposed method achieves more accurate recommendations for customers who purchase similar types of products repeatedly. Our experimental evaluation on the well-known MovieLens data set shows that our method does result in a better accuracy
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