Resolving Cold Start problem in recommendation system using demographic approach

Pandey, Anand; Rajpoot, Dharmveer Singh

doi:10.1109/icspcom.2016.7980578

Cited by 22 publications

(19 citation statements)

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“…When the number of movies rated within a system increases, due to limited resources and computational complexity of existing algorithms, the recommender system cannot function efficiently. [4]. In terms of computational resources, many modern technologies such as cloud computing can be used to scale the system.…”

Section: Scalabilitymentioning

confidence: 99%

“…A situation may arise where users are not matched to other users within a system to produce a user profile. Without user profiles it becomes practically impossible to generate recommendations [4].…”

Section: Grey Sheepmentioning

confidence: 99%

“…1 gives an overall idea of the system pipeline design. We preprocess the data using WEKA [4]. After this, the attributes are given as an input to a clustering algorithm.…”

Section: Process Pipelinementioning

confidence: 99%

“…Decision stumps work on the principle of confidence rated predictions and work well in boosting techniques; where weak classifiers together form an overall strong classifier [18]. In paper [4], decision tree is used as a standalone classification model for cluster assignment.…”

Section: Decision Stumpmentioning

confidence: 99%

“…The steps of the J48 algorithm are as follows: J48 is a robust classification algorithm which works extremely well with continuous and distinct data which also allows for data pruning after the model is created. In paper [4], J48 is also used as a standalone classification model for cluster assignment.…”

Section: J48mentioning

confidence: 99%

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Resolving Cold Start Problem Using User Demographics and Machine Learning Techniques for Movie Recommender Systems

Motadoo

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There is a substantial increase in demand for recommender systems which have applications in a variety of domains. The goal of recommendations is to provide relevant choices to users. In practice, there are multiple methodologies in which recommendations take place like Collaborative Filtering (CF), Content-based filtering and Hybrid approach. For this paper, we will consider these approaches to be traditional approaches. The advantages of these approaches are in their design, functionality and efficiency. However, they do suffer from some major problems such as data sparsity, scalability and cold start to name a few. Among these problems, cold start is an intriguing area which has been plaguing recommender systems. Cold start problem occurs when the recommender system is not able to recommend new users/items since there is data sparsity. Researchers have formulated innovative techniques to alleviate cold start and the existing research conducted in this area is tremendous since the problem materializes in different use cases. Cold start is categorized into three problems. The first problem is when new users needs product recommendations from the system. The second problem is when new products listed in the system need to be recommended to existing users. The last problem is when new users and new products are present and the recommender engine needs to generate relevant recommendations. In this thesis, we concentrate on the first problem, where a user who is completely new to the system needs quality recommendations. We use a movie recommendation platform as our use case to analyze user demographics and find similarities between existing and new users to produce relevant recommendations.

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Section: Scalabilitymentioning

confidence: 99%

Section: Grey Sheepmentioning

confidence: 99%

“…1 gives an overall idea of the system pipeline design. We preprocess the data using WEKA [4]. After this, the attributes are given as an input to a clustering algorithm.…”

Section: Process Pipelinementioning

confidence: 99%

Section: Decision Stumpmentioning

confidence: 99%

Section: J48mentioning

confidence: 99%

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