EXTRA: Explanation Ranking Datasets for Explainable Recommendation
Autor: | Yongfeng Zhang, Li Chen, Lei Li |
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Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Computer science business.industry Recommender system Machine learning computer.software_genre Computer Science - Information Retrieval Variety (cybernetics) Locality-sensitive hashing Ranking (information retrieval) Similarity (psychology) Benchmark (computing) Code (cryptography) Learning to rank Artificial intelligence business computer Information Retrieval (cs.IR) |
Zdroj: | SIGIR |
Popis: | Recently, research on explainable recommender systems has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, their evaluation approaches vary from model to model, which makes it quite difficult to compare the explainability of different models. To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by ranking-oriented metrics. Constructing such datasets, however, poses great challenges. First, user-item-explanation triplet interactions are rare in existing recommender systems, so how to find alternatives becomes a challenge. Our solution is to identify nearly identical sentences from user reviews. This idea then leads to the second challenge, i.e., how to efficiently categorize the sentences in a dataset into different groups, since it has quadratic runtime complexity to estimate the similarity between any two sentences. To mitigate this issue, we provide a more efficient method based on Locality Sensitive Hashing (LSH) that can detect near-duplicates in sub-linear time for a given query. Moreover, we make our code publicly available to allow researchers in the community to create their own datasets. |
Databáze: | OpenAIRE |
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