Utilizing Association Rules for Improving the Performance of Collaborative Filtering

2012 ◽  
Vol 3 (2) ◽  
pp. 14-28 ◽  
Author(s):  
Zainab Khanzadeh ◽  
Mehregan Mahdavi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Association Rules ◽  
Cold Start ◽  
User Preferences ◽  
Experimental Results ◽  
Internet Technology ◽  
Promising Technique ◽  
Novel Approach ◽  
Better Than

Internet technology has rapidly grown during the last decades. Presently, users are faced with a great amount of information and they need help to find appropriate items in the shortest possible time. Recommender systems were introduced to overcome this problem of overloaded information. They recommend items of interest to users based on their expressed preferences. Major e-commerce companies try to use this technology to increase their sales. Collaborative Filtering is the most promising technique in recommender systems. It provides personalized recommendations according to user preferences. But one of the problems of Collaborative Filtering is cold-start. The authors provide a novel approach for solving this problem through using the attributes of items in order to recommend items to more people for improving e-business activities. The experimental results show that the proposed method performs better than existing methods in terms of the number of generated recommendations and their quality.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals A Hybrid Deep Collaborative Filtering Approach for Recommender Systems

2021 ◽  
Author(s):  
Kirubahari R ◽  
Miruna Joe Amali S
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Cold Start ◽  
Singular Value ◽  
User Preferences ◽  
Boltzmann Machine ◽  
Latent Features ◽  
Value Decomposition ◽  
Cold Start Problem

Abstract Recommender Systems (RS) help the users by showing better products and relevant items efficiently based on their likings and historical interactions with other users and items. Collaborative filtering is one of the most powerful technique of recommender system and provides personalized recommendation for users by prediction rating approach. Many Recommender Systems generally model only based on user implicit feedback, though it is too challenging to build RS. Conventional Collaborative Filtering (CF) techniques such as matrix decomposition, which is a linear combination of user rating for an item with latent features of user preferences, but have limited learning capacity. Additionally, it has been suffering from data sparsity and cold start problem due to insufficient data. In order to overcome these problems, an integration of conventional collaborative filtering with deep neural networks is proposed. A Weighted Parallel Deep Hybrid Collaborative Filtering based on Singular Value Decomposition (SVD) and Restricted Boltzmann Machine (RBM) is proposed for significant improvement. In this approach a user-item relationship matrix with explicit ratings is constructed. The user - item matrix is integrated to Singular Value Decomposition (SVD) that decomposes the matrix into the best lower rank approximation of the original matrix. Secondly the user-item matrix is embedded into deep neural network model called Restricted Boltzmann Machine (RBM) for learning latent features of user- item matrix to predict user preferences. Thus, the Weighted Parallel Deep Hybrid RS uses additional attributes of user - item matrix to alleviate the cold start problem. The proposed method is verified using two different movie lens datasets namely, MovieLens 100K and MovieLens of 1M and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate better prediction compared to other techniques in terms of accuracy.

scholarly journals Ensemble Based Hybrid Recommender Systems

2020 ◽  
Vol 9 (3) ◽  
pp. 826-833
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Nearest Neighbor ◽  
Demographic Data ◽  
Weighted Average ◽  
Cold Start ◽  
User Preferences ◽  
Vector Method ◽  
Hybrid Recommender Systems ◽  
Hybrid Recommender

In the past few years, the advent of computational and prediction technologies has spurred a lot of interest in recommendation research. Content-based recommendation and collaborative filtering are two elementary ways to build recommendation systems. In a content based recommender system, products are described using keywords and a user profile is developed to enlist the type of products the user may like. Widely used Collaborative filtering recommender systems provide recommendations based on similar user preferences. Hybrid recommender systems are a blend of content-based and collaborative techniques to harness their advantages to maximum. Although both these methods have their own advantages, they fail in ‘cold start’ situations where new users or products are introduced to the system, and the system fails to recommend new products as there is no usage history available for these products. In this work we work on MovieLens 100k dataset to recommend movies based on the user preferences. This paper proposes a weighted average method for combining predictions to improve the accuracy of hybrid models. We used standard error as a measure to assign the weights to the classifiers to approximate their participation in predicting the recommendations. The cold start problem is addressed by including demographic data of the user by using three approaches namely Latent Vector Method, Bayesian Weighted Average, and Nearest Neighbor Algorithm.

scholarly journals Addressing Cold Start Problem in Collaborative Filtering using Demographic Data with Entropy-based Methodology

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Victor T. Odumuyiwa ◽  
Olalekan P. Oloba
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Demographic Data ◽  
Cold Start ◽  
Experimental Results ◽  
Demographic Approach ◽  
Cold Start Problem

Collaborative filtering based recommender systems (RS) are faced with cold start problem. This problem arises when the RS does not have enough information or opinion about a person or about a product and therefore cannot make recommendation for such person. In this paper, the demographic data of the user such as age, gender, and occupation are utilized as additional sources together with existing users’ rating to tackle the cold start problem by employing the entropy-based methodology to determine the degree of predictability.  Experimental results on MovieLens dataset showed that the proposed method gives higher accuracy than other existing demographic based methods. Keywords— Cold Start, Collaborative Filtering, Entropy, Demographic Approach, Recommender Systems

A Network-Based Multi-Dimensional Recommendation Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 1218-1222
Author(s):  
Xiang Yun Xiong ◽  
Yu Chen Fu ◽  
Zhao Qing Liu
Keyword(s):  
Collaborative Filtering ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Recommendation Algorithm ◽  
Better Than

Personalized recommendation based on bipartite network has attracted more and more attention. Its obviously better than CF (Collaborative Filtering). In this paper, we propose a multi-dimensional recommendation algorithm called BNPM. It combines item-based, user-based and category-based recommendation model to improve recommendation quality. The experimental results show that the algorithm can improve the diversity and reduce the popularity on the base of holding the accuracy of the recommendation

scholarly journals Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 270-278
Author(s):  
Yang Xu ◽  
Lei Zhu ◽  
Zhiyong Cheng ◽  
Jingjing Li ◽  
Jiande Sun
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
Feature Fusion ◽  
Computation Time ◽  
Cold Start ◽  
User Preferences ◽  
Low Rank ◽  
Specific Content ◽  
Hash Codes ◽  
Content Feature

Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the state-of-the-arts on various aspects.

scholarly journals Semantic Distance Spreading Across Entities in Linked Open Data

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Sultan Alfarhood ◽  
Susan Gauch ◽  
Kevin Labille
Keyword(s):  
Recommender Systems ◽  
Linked Data ◽  
Open Data ◽  
Cold Start ◽  
Semantic Distance ◽  
Linked Open Data ◽  
Experimental Results ◽  
Distance Calculation ◽  
Cold Start Problem

Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data Semantic Distance (LDSD), considers nearby resources to be recommended by calculating a semantic distance between resources. The LDSD approach, however, has some drawbacks such as its inability to measure the semantic distance resources that are not directly linked to each other. In this paper, we first propose another variation of the LDSD approach, called wtLDSD, by extending indirect distance calculations to include the effect of multiple links of differing properties within LOD, while prioritizing link properties. Next, we introduce an approach that broadens the coverage of LDSD-based approaches beyond resources that are more than two links apart. Our experimental results show that approaches we propose improve the accuracy of the LOD-based recommendations over our baselines. Furthermore, the results show that the propagation of semantic distance calculation to reflect resources further away in the LOD graph extends the coverage of LOD-based recommender systems.

FIT: A Fast Algorithm for Discovering Frequent Itemsets in Large Databases

2005 ◽  
Vol 1 (3) ◽  
pp. 129-135
Author(s):  
Jun Luo ◽  
Sanguthevar Rajasekaran
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fast Algorithm ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Important Data ◽  
Computational Performance ◽  
Large Databases ◽  
Intersection Operation ◽  
Better Than

Association rules mining is an important data mining problem that has been studied extensively. In this paper, a simple but Fast algorithm for Intersecting attributes lists using hash Tables (FIT) is presented. FIT is designed for efficiently computing all the frequent itemsets in large databases. It deploys an idea similar to Eclat but has a much better computational performance than Eclat due to two reasons: 1) FIT makes fewer total number of comparisons for each intersection operation between two attributes lists, and 2) FIT significantly reduces the total number of intersection operations. Our experimental results demonstrate that the performance of FIT is much better than that of Eclat and Apriori algorithms.

scholarly journals Further Improvement on Two-Way Cooperative Collaborative Filtering Approaches for the Binary Market Basket Data

2021 ◽  
Vol 11 (19) ◽  
pp. 8977
Author(s):  
Wook-Yeon Hwang ◽  
Jong-Seok Lee
Keyword(s):  
Logistic Regression ◽  
Collaborative Filtering ◽  
Random Forests ◽  
Pearson Correlation ◽  
Main Idea ◽  
Experimental Results ◽  
Market Basket ◽  
Regression Approach ◽  
Cold Start Problem ◽  
Better Than

Two-way cooperative collaborative filtering (CF) has been known to be crucial for binary market basket data. We propose an improved two-way logistic regression approach, a Pearson correlation-based score, a random forests (RF) R-square-based score, an RF Pearson correlation-based score, and a CF scheme based on the RF R-square-based score. The main idea is to utilize as much predictive information as possible within the two-way prediction in order to cope with the cold-start problem. All of the proposed methods work better than the existing two-way cooperative CF approach in terms of the experimental results.

scholarly journals Session-Based Recommender System for Sustainable Digital Marketing

2019 ◽  
Vol 11 (12) ◽  
pp. 3336 ◽  
Author(s):  
Hyunwoo Hwangbo ◽  
Yangsok Kim
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
High Performance ◽  
Marketing Strategies ◽  
Feature Weighting ◽  
Experimental Results ◽  
Digital Marketing ◽  
Chi Square ◽  
Fashion Products

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

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