Conclusion Introduction Recommendation System { "asin": - - PowerPoint PPT Presentation

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Conclusion Introduction Recommendation System { "asin": - - PowerPoint PPT Presentation

Dec 28, 2023 279 likes •488 views

1 Introduction 2 Method 3 Experiment 4 Conclusion Introduction Recommendation System { "asin": "0000031852", "title": "Girls Ballet Tutu Hot Pink", "price": 3.17, "related":

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1

Introduction

2

Method

3

Experiment

4

Conclusion

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Introduction

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Recommendation System

{ "asin": "0000031852", "title": "Girls Ballet Tutu Hot Pink", "price": 3.17, "related": { "also_bought":["B00JHONN1S", ...], "also_viewed":["B002BZX8Z6", ...], "bought_together": ["B002BZX8Z6"] }, "salesRank": {"Toys & Games": 211836}, "brand": "Coxlures", "categories": [["Sports & Outdoors"]] }

Predict behavior

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Motivation

A B

usefulness

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Motivation

C

useful

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Method

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Framework

User reviews Item reviews

Great Product. I love this Product and my children will too. I cant wait til Christmas to Give them their present ! … …

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Hierarchical Attention based Neural Network

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Intra-review

𝑏𝑘

∗ = 𝑋 𝑏 𝑈𝑆𝑓𝑀𝑉 𝑋 ℎℎ𝑘 + 𝑋 𝑣𝑤𝑣,𝑗 + 𝑐1 + 𝑐2

Interaction vector Attention Mechanism

Pre-train word embedding

𝜗ℝ𝐿

,

Great Product. I love this Product and my children will too. I cant wait til Christmas to Give them their present !

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Attention for inter-review External Memory

Inter-review

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Prediction Layer

Objective function

Fully-Connected

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Experiment

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Dataset

Amazon Product Data

  • May 1996 – July 2014
  • Each users and items has at least 5 reviews
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Baseline Methods

CNN Based

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Comparison with baseline methods

Evaluation Method

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Explanation Analysis of HANN

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Explanation Analysis of HANN

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Conclusion

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Conclusion

  • We design a hierarchical attention framework to

learn the interaction between users and items from reviews to construct an explainable recommendation system.

  • The well-designed hierarchical attention

mechanism helps the model capture user profiles and item profiles, making them more explainable and reasonable, and ultimately leads to improvements in rating prediction.