scholarly journals Recommendation System for Video Streaming Websites Based on User Feedback

2019 ◽  
Vol 8 (6) ◽  
pp. 1317-1320
Keyword(s):  
Video Streaming ◽  
Hybrid Algorithm ◽  
Recommendation System ◽  
Business Organizations ◽  
Filtering Algorithm ◽  
User Interests ◽  
Past Behavior ◽  
Semantic Factor ◽  
Modern Era ◽  
Content Based Filtering

Now a days world business organizations are mostly focused in e-commerce for enlightening their business as well as supporting their users. In the modern era, vast amount of information is generated from the internet which is open to the users. Recommendation system is midway between internet and user which expects user interests. This paper primarily focusing on developing the recommendation system for video streaming sites. The recommendation engine mainly works on content based, collaborative based filtering algorithms. But both has limitations in their own way. The content-based filtering has a shortcoming that, it restricts recommendations of the items that are of same category. Whereas in the collaborative-based filtering algorithm, it doesn’t recommend items based on the user’s past behavior. So, this system is developed using a hybrid algorithm to overcome the problems of above two algorithms by retrieving feedback from the users and calculating semantic factor from the feedback to improve the efficiency of the recommendation system. So that lets companies can better understand the user, make available personalized stores, and increases the satisfaction of the customer and their loyalty

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

scholarly journals Hierarchical Context Enabled Recurrent Neural Network for Recommendation

2019 ◽  
Vol 33 ◽  
pp. 4983-4991 ◽  
Author(s):  
Kyungwoo Song ◽  
Mingi Ji ◽  
Sungrae Park ◽  
Il-Chul Moon
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Sequential Model ◽  
User Interests ◽  
Gate Structure ◽  
Term Dependency ◽  
User History ◽  
Personal Interests ◽  
Over Time

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our interest drift assumption. As we suggest a new RNN structure, we support HCRNN with a complementary bi-channel attention structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.

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