A restaurant recommendation approach with the contextual information

Inherent Problem ◽

Scoring Matrix ◽

Time Price ◽

Potential Customers

Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location and the current time of customers should also be considered to facilitate restaurants to find potential customers and give accurate and timely recommendations. However, the existing recommendation approaches often lack the consideration of the influence of time and location. Besides, the data sparsity is an inherent problem in the collaborative filtering algorithm. To address these problems, this paper proposes a recommendation approach which combines the contextual information including time, price and location. Instead of constructing the user-restaurant scoring matrix, the proposed approach clusters price tags and generates the user-price scoring matrix to alleviate the sparsity of data. The experiment on Foursquare dataset shows that the proposed approach has a better performance than traditional ones.

Novel Multidimensional Collaborative Filtering Algorithm Based on Improved Item Rating Prediction

Scientific Programming ◽

10.1155/2021/2592604 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Tongyan Li ◽

Yingxiang Li ◽

Chen Yi-Ping Phoebe

Keyword(s):

Collaborative Filtering ◽

Sparse Data ◽

Current Data ◽

Prediction Algorithm ◽

Filtering Algorithm ◽

Scoring Matrix ◽

Rating Prediction ◽

User Ratings

Current data has the characteristics of complexity and low information density, which can be called the information sparse data. However, a large amount of data makes it difficult to analyse sparse data with traditional collaborative filtering recommendation algorithms, which may lead to low accuracy. Meanwhile, the complexity of data means that the recommended environment is affected by multiple dimensional factors. In order to solve these problems efficiently, our paper proposes a multidimensional collaborative filtering algorithm based on improved item rating prediction. The algorithm considers a variety of factors that affect user ratings; then, it uses the penalty to account for users’ popularity to calculate the degree of similarity between users and cross-iterative bi-clustering for the user scoring matrix to take into account changes in user’s preferences and improves on the traditional item rating prediction algorithm, which considers user ratings according to multidimensional factors. In this algorithm, the introduction of systematic error factors based on statistical learning improves the accuracy of rating prediction, and the multidimensional method can solve data sparsity problems, enabling the strongest relevant dimension influencing factors with association rules to be found. The experiment results show that the proposed algorithm has the advantages of smaller recommendation error and higher recommendation accuracy.

CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

International Journal of Modern Physics C ◽

10.1142/s0129183110015786 ◽

2010 ◽

Vol 21 (10) ◽

pp. 1217-1227 ◽

Cited By ~ 24

Author(s):

WEI ZENG ◽

MING-SHENG SHANG ◽

QIAN-MING ZHANG ◽

LINYUAN LÜ ◽

TAO ZHOU

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

High Accuracy ◽

Personalized Recommendation ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

High Diversity ◽

Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

Research of Data Sparsity Based on Collaborative Filtering Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.856 ◽

2013 ◽

Vol 462-463 ◽

pp. 856-860

Author(s):

Li Min Liu ◽

Peng Xiang Zhang ◽

Le Lin ◽

Zhi Wei Xu

Keyword(s):

Collaborative Filtering ◽

Experimental Results ◽

Recommendation Algorithm ◽

Data Sparsity ◽

Filtering Algorithm ◽

Data Sparseness ◽

Scoring Matrix ◽

Sparsity Problem ◽

Improved Algorithm

During the traditional collaborative filtering recommendation algorithm be impacted by itself data sparseness problem. It can not provide accurate recommendation result. In this paper, Using traditional collaborative filtering algorithm and the concept of similar level, take advantage of the idea of data populating to solve sparsity problem, then using the Weighted Slope One algorithm to recommend calculating. Experimental results show that the improved algorithm solved the problem of the recommendation results of low accuracy because of the sparse scoring matrix, and it improved the algorithm recommended results to a certain extent.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Enhanced Recommendation System for E-commerce Applications

10.35940/ijitee.h6914.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 473-478

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Web Applications ◽

Online Shopping ◽

User Behavior ◽

Filtering Algorithm ◽

Special Subset ◽

And Behavior

Recommendation algorithms play a quintessential role in development of E-commerce recommendation system, Where in Collaborative filtering algorithm is a major contributor for most recommendation systems since they are a flavor of KNN algorithm specifically tailored for E-commerce Web Applications, the main advantages of using CF algorithms are they are efficient in capturing collective experiences and behavior of e-commerce customers in real time, But it is noted that , this results in the phenomenon of Mathew effect, Wherein only popular products are listed into the recommendation list and lesser popular items tend to become even more scarce. Hence this results in products which are already familiar to users being discovered redundantly, thus potential discovery of niche and new items in the e-commerce application is compromised. To address this issue , this paper throws light on user behavior on the online shopping platform , accordingly a novel selectivity based collaborative filtering algorithm is proposed with innovator products that can recommend niche items but less popular products to users by introducing the concept of collaborative filtering with consumer influencing capability. Specifically, innovator products are a special subset of products which are less popular/ have received less traction from users but are genuinely of higher quality, therefore, these aforementioned products can be captured in the recommendation list via innovator-recognition table, achieving the balance between popularity and practicability for the user