Personalized Commodity Recommendations of Retail Business Using User Feature Based Collaborative Filtering

Wang, Feiran; Wen, Yiping; Guo, Ting; Chen, Jinjun; Cao, Buqing

doi:10.1109/bdcloud.2018.00051

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…A hybrid system that can detect the situation such as user cold start or item cold start and apply suitable algorithm is needed in order to achieve good result [24,39,40]. As we know, most algorithms cannot handle multiple scenario in one algorithm.…”

Section: Recommender System In Retailer Domain or E-commercementioning

confidence: 99%

Recommender System for Retail Domain

Chew

Haw

Subramaniam

2020

Proceedings of the 12th International Conference on Computer Modeling and Simulation

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Recommender system has been developed as a useful tool especially when we reached the era of big data and in the meanwhile the internet has been overwhelming with lots of choices. There is a need for people to filter the information to search for their needs and wants efficiently. E-commerce website such as Amazon and Netflix have been using recommender system to build and boost their sales through the personalization recommendation. With the success in the e-commerce area, researchers are keen on finding a method to boost traditional offline retailer sales thru the recommender system. Therefore, in this paper, we introduced the existing recommender system and discuss the method of filtering of each method. Then, we provide the overview of the recent paper in retailer and e-commerce domain to provide the insight and trends such as the filtering techniques and evaluation metric used. Several possible research direction has been discussed based on the current trends and problems. CCS Concepts• Information systems➝Information retrieval➝Retrieval tasks and goals➝Recommender systems.

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Section: Recommender System In Retailer Domain or E-commercementioning

confidence: 99%

Recommender System for Retail Domain

Chew

Haw

Subramaniam

2020

Proceedings of the 12th International Conference on Computer Modeling and Simulation

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“…Different from the "purchaseevaluation" in the e-commerce platform, the low cost of the key operation (click the button) lead to users not having the habit of commenting on a recruiting unit every time they submit a resume. Therefore, for a recruiting unit, its review text will be less than other fields, such as e-commerce platforms, in terms of the overall number, per capita number of reviews, and the number of review words [22,23]. (3) Insufficiency of the recommendation technology based on the resume content: Although this method has made great progress in the unstructured extraction and understanding of the semantics [24,25], it ignores preference support on features.…”

Section: Introductionmentioning

confidence: 99%

A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition

Mao,

Cheng,

Shi

2023

Applied Sciences

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In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users’ attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of “job seeker user-position-attention layered”. Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms.

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