A new similarity function for selecting neighbors for each target item in collaborative filtering

2013 ◽  
Vol 37 ◽  
pp. 146-153 ◽  
Author(s):  
Keunho Choi ◽  
Yongmoo Suh
Keyword(s):  
Collaborative Filtering ◽  
Target Item ◽  
Similarity Function

Popularised Similarity Function for Effective Collaborative Filtering Recommendations

2020 ◽  
Vol 10 (1) ◽  
pp. 34-47
Author(s):  
Abba Almu ◽  
Abubakar Roko ◽  
Aminu Mohammed ◽  
Ibrahim Saidu
Keyword(s):  
Collaborative Filtering ◽  
Experimental Studies ◽  
Similarity Function ◽  
Similarity Functions ◽  
Rating Prediction

The existing similarity functions use the user-item rating matrix to process similar neighbours that can be used to predict ratings to the users. However, the functions highly penalise high popular items which lead to predicting items that may not be of interest to active users due to the punishment function employed. The functions also reduce the chances of selecting less popular items as similar neighbours due to the items with common ratings used. In this article, a popularised similarity function (pop_sim) is proposed to provide effective recommendations to users. The pop_sim function introduces a modified punishment function to minimise the penalty on high popular items. The function also employs a popularity constraint which uses ratings threshold to increase the chances of selecting less popular items as similar neighbours. The experimental studies indicate that the proposed pop_sim is effective in improving the accuracy of the rating prediction in terms of not only lowering the MAE but also the RMSE.

A Hybrid Document Recommender Algorithm Based on Random Walk

2013 ◽  
Vol 336-338 ◽  
pp. 2270-2276
Author(s):  
Zhi Ping Zhang ◽  
Lin Na Li ◽  
Hai Yan Yu
Keyword(s):  
Random Walk ◽  
Collaborative Filtering ◽  
Digital Library ◽  
Recommender System ◽  
Target Item ◽  
The Other ◽  
National Science ◽  
Recommender Algorithm ◽  
Valuable Service ◽  
Cold Start Problem

Research shows that recommendations comprise a valuable service for users of a digital library. We proposed a hybrid document recommender system based on random walk. It builds correlation network among users based on the conditional probability in order to solve the sparsity of collaborative filtering. On the other hand, it computes the rating of source user for target item not only based on the neighborhoods’ ratings for target item but also based on the neighborhoods’ ratings for item which is most similar to target item. This can solve the cold start problem of recommender systems. We performed an evaluation on the dataset of National Science and Technology Library. Experimental results illustrate the superiority of the proposed method.

scholarly journals Abnormal Profiles Detection Based on Time Series and Target Item Analysis for Recommender Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhou ◽  
Junhao Wen ◽  
Min Gao ◽  
Haijun Ren ◽  
Peng Li
Keyword(s):  
Time Series ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Target Item ◽  
Item Analysis ◽  
Work Related ◽  
Shilling Attack ◽  
Short Period ◽  
Group Attributes ◽  
Statistical Metrics

Collaborative filtering (CF) recommenders are vulnerable to shilling attacks designed to affect predictions because of financial reasons. Previous work related to robustness of recommender systems has focused on detecting profiles. Most approaches focus on profile classification but ignore the group attributes among shilling attack profiles. Attack profiles are injected in a short period in order to push or nuke a specific target item. In this paper, we propose a method for detecting suspicious ratings by constructing a time series. We reorganize all ratings on each item sorted by time series. Each time series is examined and suspected rating segments are checked. Then we use techniques we have studied in previous study to detect shilling attacks in these anomaly rating segments using statistical metrics and target item analysis. We show in experiments that our proposed method can be effective and less time consuming at detecting items under attacks in big datasets.

Using the beta distribution technique to detect attacked items from collaborative filtering

2021 ◽  
Vol 25 (1) ◽  
pp. 121-137
Author(s):  
Ping-Yu Hsu ◽  
Jui-Yi Chung ◽  
Yu-Chin Liu
Keyword(s):  
Collaborative Filtering ◽  
Beta Distribution ◽  
Detection Rate ◽  
Recommendation System ◽  
Target Item ◽  
Multiple Target ◽  
The Past ◽  
User Data

A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user’s trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.

scholarly journals Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

2016 ◽  
Vol 11 (5) ◽  
pp. 631 ◽  
Author(s):  
Keunho Choi ◽  
Yongmoo Suh ◽  
Donghee Yoo
Keyword(s):  
Collaborative Filtering ◽  
Online Shopping ◽  
Recommendation Systems ◽  
Shopping Mall ◽  
Similarity Function ◽  
Shopping Malls ◽  
Transaction Data ◽  
User Similarity ◽  
Sparsity Problem ◽  
Rating Information

Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique.

A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

2021 ◽  
pp. 016555152097987
Author(s):  
Yong Wang ◽  
Xuhui Zhao ◽  
Zhiqiang Zhang ◽  
Leo Yu Zhang
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Target Item ◽  
Prediction Method ◽  
Sparse Data ◽  
Hellinger Distance ◽  
New Strategy ◽  
Similar Item ◽  
Benchmark Datasets ◽  
Collaborative Filtering Algorithm

The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.

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