Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects

Ni, Jianmo; Li, Jiacheng; McAuley, Julian

doi:10.18653/v1/d19-1018

Cited by 783 publications

(451 citation statements)

References 30 publications

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“…Electronics is another review dataset collected from the Electronics category on Amazon with Clothing as an auxiliary category. This dataset is built on top of the public Amazon 2018 Dataset [24] and further processed to facilitate the research goals in this paper. We regard the gender as the target marketing bias on this dataset.…”

Section: Electronicsmentioning

confidence: 99%

Addressing Marketing Bias in Product Recommendations

Wan

Misra

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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Modern collaborative filtering algorithms seek to provide personalized product recommendations by uncovering patterns in consumerproduct interactions. However, these interactions can be biased by how the product is marketed, for example due to the selection of a particular human model in a product image. These correlations may result in the underrepresentation of particular niche markets in the interaction data; for example, a female user who would potentially like motorcycle products may be less likely to interact with them if they are promoted using stereotypically 'male' images.In this paper, we first investigate this correlation between users' interaction feedback and products' marketing images on two realworld e-commerce datasets. We further examine the response of several standard collaborative filtering algorithms to the distribution of consumer-product market segments in the input interaction data, revealing that marketing strategy can be a source of bias for modern recommender systems. In order to protect recommendation performance on underrepresented market segments, we develop a framework to address this potential marketing bias. Quantitative results demonstrate that the proposed approach significantly improves the recommendation fairness across different market segments, with a negligible loss (or better) recommendation accuracy.

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Section: Electronicsmentioning

confidence: 99%

Addressing Marketing Bias in Product Recommendations

Wan

Misra

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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“…Music: We use the "CDs and Vinyl" subset of the publicly available Amazon reviews 13 [21] dataset which contains 2.3m interactions. We extract ratings, reviews and genres for music albums.…”

Section: Data Sourcesmentioning

confidence: 99%

What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation

Penha

Hauff

2020

Fourteenth ACM Conference on Recommender Systems

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Heavily pre-trained transformer models such as BERT have recently shown to be remarkably powerful at language modelling by achieving impressive results on numerous downstream tasks. It has also been shown that they are able to implicitly store factual knowledge in their parameters after pre-training. Understanding what the pre-training procedure of LMs actually learns is a crucial step for using and improving them for Conversational Recommender Systems (CRS). We first study how much off-the-shelf pre-trained BERT "knows" about recommendation items such as books, movies and music. In order to analyze the knowledge stored in BERT's parameters, we use different probes (i.e., tasks to examine a trained model regarding certain properties) that require different types of knowledge to solve, namely content-based and collaborative-based. Content-based knowledge is knowledge that requires the model to match the titles of items with their content information, such as textual descriptions and genres. In contrast, collaborative-based knowledge requires the model to match items with similar ones, according to community interactions such as ratings. Both are important types of knowledge for a CRS-a system that should ideally be able to explain recommendations, match a user's textual description of their information need to relevant items, and elucidate a user's interests. We resort to BERT's Masked Language Modelling (MLM) head to probe its knowledge about the genre of items, with cloze style prompts. In addition, we employ BERT's Next Sentence Prediction (NSP) head and representations' similarity (SIM) to compare relevant and non-relevant search and recommendation query-document inputs to explore whether BERT can, without any fine-tuning, rank relevant items first. The insights we gain from these probes help us understand BERT's limitations and strengths for conversational recommendation. Finally, we study how BERT performs in a conversational recommendation downstream task. To this end, we fine-tune BERT to act as a retrieval-based conversational recommender system. Overall, our analyses and experiments show that: (i) BERT has knowledge stored in its parameters about the content of books, movies and music; (ii) it has more content-based knowledge than collaborative-based knowledge; and (iii) fails on conversational recommendation when faced with adversarial data.

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“…We have used a dataset of Amazon reviews developed by Ni et al [47], which includes reviews and product metadata along with a 5-point rating system. We have used 5-core dense subset of the dataset, such that each of the remaining users and items has 5 reviews each.…”

Section: Dataset Usedmentioning

confidence: 99%

Sentiment Analysis Using Gated Recurrent Neural Networks

et al. 2020

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Text sentiment analysis is an important and challenging task. Sentiment analysis of customer reviews is a common problem faced by companies. It is a machine learning problem made demanding due to the varying nature of sentences, different lengths of the paragraphs of text, contextual understanding, sentiment ambiguity and the use of sarcasm and comparatives. Traditional approaches to sentiment analysis use the tally or recurrence of words in a text which are allotted sentiment values by some expert. These strategies overlook the order of words and the complex different meanings they can communicate. Hence, RNNs were introduced that are effective yet challenging to train. Bi-GRUs and Bi-LSTM architectures are a recent form of RNNs which can store information about long-term dependencies in sequential data. In this work, we attempted a survey of different deep learning techniques that have been applied to sentiment classification and analysis. We have implemented the baseline models for LSTM, GRU and Bi-LSTM and Bi-GRU on an Amazon review dataset.

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Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects

Cited by 783 publications

References 30 publications

Addressing Marketing Bias in Product Recommendations

Addressing Marketing Bias in Product Recommendations

What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation

Sentiment Analysis Using Gated Recurrent Neural Networks

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