A genetic algorithm approach to recommender system cold start problem.

2021 ◽  
Author(s):  
Sanjeevan Sivapalan
Keyword(s):  
Genetic Algorithm ◽  
Recommender Systems ◽  
User Experience ◽  
Recommender System ◽  
Evaluation Method ◽  
New Products ◽  
Cold Start ◽  
Genetic Algorithm Approach ◽  
Cold Start Problem ◽  
Novel Algorithm

Recommender systems (RS) are ubiquitous and used in many systems to augment user experience to improve usability and they achieve this by helping users discover new products to consume. They, however, suffer from cold-start problem which occurs when there is not enough information to generate recommendations to a user. Cold-start occurs when a new user enters the system that we don’t know about. We have proposed a novel algorithm to make recommendations to new users by recommending outside of their preferences. We also propose a genetic algorithm based solution to make recommendations when we lack information about user and a transitive algorithm to form neighbourhood. Altogether, we developed three algorithms and tested them using they MovieLens dataset. We have found that all of our algorithms performed well during our testing using the offline-evaluation method.

scholarly journals HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation

2019 ◽  
Vol 33 ◽  
pp. 3830-3837 ◽  
Author(s):  
Liang Hu ◽  
Songlei Jian ◽  
Longbing Cao ◽  
Zhiping Gu ◽  
Qingkui Chen ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Cold Start ◽  
Data Sparsity ◽  
Real World Datasets ◽  
Item Item ◽  
Cold Start Problem

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

scholarly journals Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

2020 ◽  
pp. 56-56
Author(s):  
Minsung Hong ◽  
Jason Jung
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
Tensor Model ◽  
Auxiliary Data ◽  
Fine Grained ◽  
Inherent Structure ◽  
Fundamental Challenge ◽  
Intuitive Rules ◽  
Cold Start Problem

Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users? fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to ad15 dress the cold-start problem. However, some of the users? preferences collected are usually not complete due to users? cognitive limitation and/or unfamiliarity on item domains, which is called ?partial preferences?. The fundamental challenge and then negatively affects to accurately recommend items according to users? preferences through MCRSs. In this paper, we propose a Hypothetical Tensor Model (HTM) to leverage auxiliary data complemented through three intuitive rules dealing with user?s unfamiliarity. First, we find four patterns of partial preferences that are caused by users? unfamiliarity. And then the rules are defined by considering relationships between multi-criteria. Lastly, complemented preferences are modeled by a tensor to maintain an inherent structure of and correlations between the multi-criteria. Experiments on a TripAdvisor dataset showed that HTM improves MSE performances from 40 to 47% by comparing with other baseline methods. In particular, effective nesses of each rule regarding multi-criteria on HTM are clearly revealed.

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.

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