scholarly journals Boosting Memory-Based Collaborative Filtering Using Content-Metadata

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 561 ◽  
Author(s):  
Kyung Soo Kim ◽  
Doo Soo Chang ◽  
Yong Suk Choi
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Cold Start ◽  
Experimental Results ◽  
Significant Enhancement ◽  
Trust Network ◽  
Network Information ◽  
Personalized Services ◽  
User Content

Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment.

A Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

2013 ◽  
Vol 765-767 ◽  
pp. 630-633 ◽  
Author(s):  
Chong Lin Zheng ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding
Keyword(s):  
Life Cycle ◽  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Experimental Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Time Information ◽  
Successful Technology

Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems. However, traditional collaborative filtering recommendation algorithm does not consider the change of time information. For this problem,this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest change; Recommend according to scores by adding the weight information determined by the item life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++

2021 ◽  
pp. 1-19
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Gautam Srivastava
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Error Rate ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Matrix Decomposition ◽  
Cold Start ◽  
Singular Value ◽  
Decomposition Approach ◽  
Value Decomposition

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD)[Formula: see text] for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system’s performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD[Formula: see text], Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100[Formula: see text]K, MovieLens 1[Formula: see text]M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation ([Formula: see text]) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100[Formula: see text]K dataset ([Formula: see text], [Formula: see text]). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items.

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

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.

An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems

2012 ◽  
Vol 38 (3) ◽  
pp. 205-221 ◽  
Author(s):  
Reza Rafeh ◽  
Arash Bahrehmand
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Adaptive Approach ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Filtering Technique ◽  
Over Time ◽  
Filtering Techniques

Recommendation systems manage information overload in order to present personalized content to users based on their interests. One of the most efficient recommendation approaches is collaborative filtering, through which recommendation is based on previously rated data. Collaborative filtering techniques feature impressive solutions for suggesting favourite items to certain users. However, recommendation methods fail to reflect fluctuations in users’ behaviour over time. In this article, we propose an adaptive collaborative filtering algorithm which takes time into account when predicting users’ behaviour. The transitive relationship from one user to another is considered when computing the similarity of two different users. We predict variations of users’ preferences using their profiles. Our experimental results show that the proposed algorithm is more accurate than the classical collaborative filtering technique.

Research of User Profile Model in Personalized Search

2014 ◽  
Vol 543-547 ◽  
pp. 3364-3368
Author(s):  
Yu Yang He ◽  
Yan Tang
Keyword(s):  
Collaborative Filtering ◽  
User Profile ◽  
Cold Start ◽  
User Interest ◽  
Personalized Search ◽  
Vector Method ◽  
Personalized Services ◽  
Interest Model ◽  
User Interest Model ◽  
Cold Start Problem

For personalized service, existing user interest model primarily through the select weights Highest N keywords to represent the user interest model based on space vector method. The method of establishing the model is tend to content-based analysis methods and there is a serious "cold start" problem, cannot meet the demand for personalized services. Therefore, this paper add collaborative filtering factor in the process of establishing user interest model, and verified by experiment, after adding personalization features which make the service more obvious. In a certain extent, solve the new user's "cold start" problem.

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