Name
Title | ||
|---|---|---|
Identifying Complements and Substitutes of Products: A Neural Network Framework Based on Product Embedding. |
Abstract | ||
|---|---|---|
Complements and substitutes are two typical product relationships that deserve consideration in online product recommendation. One of the key objectives of recommender systems is to promote cross-selling, which heavily relies on recommending the appropriate type of products in specific scenarios. Research on consumer behavior has shown that consumers usually prefer substitutes in the browsing stage whereas complements in the purchasing stage. Thus, it is of great importance to identify the complementary and substitutable relationships between products. In this article, we design a neural network based framework that integrates the textual content and non-textual information of online reviews to mine product relationships. For the textual content, we utilize methods such as LDA topic modeling to represent products in a succinct form called “embedding.” To capture the semantics of complementary and substitutable relationships, we design a modeling process that transfers the product embeddings into semantic features and incorporates additional non-textual factors of product reviews. Extensive experiments are conducted to verify the effectiveness of the proposed product relationship mining model. The advantages and robustness of our model are discussed from various perspectives.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3320277 | ACM Transactions on Knowledge Discovery from Data |
Keywords | Field | DocType |
Complements,online reviews,product embedding,product recommendation,product relationship,substitutes | Recommender system,Embedding,Computer science,Consumer behaviour,Robustness (computer science),Purchasing,Artificial intelligence,Topic model,Artificial neural network,Machine learning,Semantics | Journal |
Volume | Issue | ISSN |
13 | 3 | 1556-4681 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyue Zhang | 1 | 4 | 4.14 |
| Xuan Wei | 2 | 1 | 2.04 |
| Xunhua Guo | 3 | 163 | 22.88 |
| Guoqing Chen | 4 | 912 | 71.58 |
| Qiang Wei | 5 | 284 | 26.14 |