Collaborative Filtering and Leaders' Advice Based Recommendation System for Cold Start Users

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.

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Personalized Suggestion For Music Based On Collaborative Filtering

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v9i05.4487 ◽

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.

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

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622021500310 ◽

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.

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Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start Problem

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2020040104 ◽

2020 ◽

Vol 11 (2) ◽

pp. 62-79

Author(s):

Sharon Moses J. ◽

Dhinesh Babu L.D.

Keyword(s):

Genetic Algorithm ◽

Collaborative Filtering ◽

Recommendation System ◽

Information Source ◽

Cold Start ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Real World Datasets ◽

The Impact ◽

Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a genetic algorithm-influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of the genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state of art approaches.

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Addressing Cold Start New User in Recommender System Based on Hybrid Approach: A review and bibliometric analysis

IT JOURNAL RESEARCH AND DEVELOPMENT ◽

10.25299/itjrd.2021.vol6(1).6118 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1-16

Author(s):

Nasy`an Taufiq Al Ghifari ◽

Benhard Sitohang ◽

Gusti Ayu Putri Saptawati

Keyword(s):

Collaborative Filtering ◽

Bibliometric Analysis ◽

Recommendation System ◽

Hybrid Methods ◽

Hybrid Approach ◽

Cold Start ◽

System Research ◽

Hybrid Techniques ◽

Social Network Data ◽

Internet Users

Increasing number of internet users today, the use of e-commerce becomes a very vital need. One of the keys that holds the success of the e-commerce system is the recommendation system. Collaborative filtering is the popular method of recommendation system. However, collaborative filtering still has issues including data sparsity, cold start, gray sheep, and dynamic taste. Some studies try to solve the issue with hybrid methods that use a combination of several techniques. One of the studies tried to solve the problem by building 7 blocks of hybrid techniques with various approaches. However, the study still has some problems left. In the case of cold start new users, actually, the method in the study has handled it with matrix factorizer block and item weight. But it will produce the same results for all users so that the resulting personalization is still lacking. This study aims to map an overview of the themes of recommendation system research that utilizes bibliometric analysis to assess the performance of scientific articles while exposing solution opportunities to cold start problems in the recommendation system. The results of the analysis showed that cold start problems can be solved by utilizing social network data and graph approaches.

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Multi-criteria based Item Recommendation Methods

Rekayasa ◽

10.21107/rekayasa.v12i2.5913 ◽

2019 ◽

Vol 12 (2) ◽

pp. 78-84

Author(s):

Noor Ifada ◽

Syafrurrizal Naridho ◽

Mochammad Kautsar Sophan

Keyword(s):

Collaborative Filtering ◽

Empirical Analysis ◽

Recommendation System ◽

Cold Start ◽

The Other ◽

Other Hand ◽

Rating Prediction ◽

The Empirical Analysis

This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction of all criteria; and generating the top- item recommendations. The multi-criteria based item recommendation methods are varied and labelled based on what approach is implemented to predict the rating per criterion, i.e., Collaborative Filtering (CF), Content-based (CB), and Hybrid. For the experiments, we generate two variations of datasets to represent the normal and cold-start conditions on the multi-criteria item recommendation system. The empirical analysis suggests that Hybrid and CF are best implemented on the normal and cold-start item conditions, respectively. On the other hand, CB should never be (solely) implemented in a multi-criteria based item recommendation system on any conditions.

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A New Recommendation Algorithm Based on User’s Dynamic Information in Complex Social Network

Mathematical Problems in Engineering ◽

10.1155/2015/281629 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 7

Author(s):

Jiujun Cheng ◽

Yingbo Liu ◽

Huiting Zhang ◽

Xiao Wu ◽

Fuzhen Chen

Keyword(s):

Social Network ◽

Collaborative Filtering ◽

Recommendation System ◽

Cold Start ◽

Dynamic Information ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Response Information ◽

Collaborative Filtering Algorithm ◽

Information Dynamic

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.

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Collaborative filtering and deep learning based recommendation system for cold start items

Expert Systems with Applications ◽

10.1016/j.eswa.2016.09.040 ◽

2017 ◽

Vol 69 ◽

pp. 29-39 ◽

Cited By ~ 209

Author(s):

Jian Wei ◽

Jianhua He ◽

Kai Chen ◽

Yi Zhou ◽

Zuoyin Tang

Keyword(s):

Deep Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Cold Start

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Identifying Attack Models for Securing Cluster-Based Recommendation System

Recent Patents on Engineering ◽

10.2174/1872212114666200403091053 ◽

2020 ◽

Vol 14 ◽

Author(s):

Amreen Ahmad ◽

Tanvir Ahmad ◽

Ishita Tripathi

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

Recommendation System ◽

Relevant Information ◽

System Function ◽

Recommendation Algorithm ◽

Underlying Network ◽

Entire Dataset ◽

Attack Models ◽

Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

Sustainability ◽

10.3390/su13137156 ◽

2021 ◽

Vol 13 (13) ◽

pp. 7156

Author(s):

Kyoung Jun Lee ◽

Yu Jeong Hwangbo ◽

Baek Jeong ◽

Ji Woong Yoo ◽

Kyung Yang Park

Keyword(s):

Collaborative Filtering ◽

High Performance ◽

Recommendation System ◽

Small And Medium Enterprises ◽

Personal Information ◽

Business Environment ◽

Share Data ◽

Artificial Intelligence Technology ◽

Medium Enterprises ◽

Start Ups

Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.

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