Document Type
Article
Publication Date
12-1-2021
Publication Title
Social Network Analysis and Mining
Volume
11
Issue
1
Keywords
Clickstream data, Cold start, Collaborative filtering, E-commerce, Historical purchases, Recommendation systems, Semantics, Sequential model, Sequential pattern mining, Sparsity, TF-IDF, Vector space model
Abstract
Collaborative Filtering (CF)-based recommendation methods suffer from (i) sparsity (have low user–item interactions) and (ii) cold start (an item cannot be recommended if no ratings exist). Systems using clustering and pattern mining (frequent and sequential) with similarity measures between clicks and purchases for next-item recommendation cannot perform well when the matrix is sparse, due to rapid increase in number of items. Additionally, they suffer from: (i) lack of personalization: patterns are not targeted for a specific customer and (ii) lack of semantics among recommended items: they can only recommend items that exist as a result of a matching rule generated from frequent sequential purchase pattern(s). To better understand users’ preferences and to infer the inherent meaning of items, this paper proposes a method to explore semantic associations between items obtained by utilizing item (products’) metadata such as title, description and brand based on their semantic context (co-purchased and co-reviewed products). The semantics of these interactions will be obtained through distributional hypothesis, which learns an item’s representation by analyzing the context (neighborhood) in which it is used. The idea is that items co-occurring in a context are likely to be semantically similar to each other (e.g., items in a user purchase sequence). The semantics are then integrated into different phases of recommendation process such as (i) preprocessing, to learn associations between items, (ii) candidate generation, while mining sequential patterns and in collaborative filtering to select top-N neighbors and (iii) output (recommendation). Experiments performed on publically available E-commerce data set show that the proposed model performed well and reflected user preferences by recommending semantically similar and sequential products.
DOI
10.1007/s13278-021-00784-6
ISSN
18695450
E-ISSN
18695469
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Nasir, Mahreen; Ezeife, C. I.; and Gidado, Abdulrauf. (2021). Improving e-commerce product recommendation using semantic context and sequential historical purchases. Social Network Analysis and Mining, 11 (1).
https://scholar.uwindsor.ca/computersciencepub/66