Subjective Search Intent Predictions using Customer Reviews

Boteanu, Adrian; Dutile, Emily; Kiežun, Adam; Artzi, Shay

doi:10.1145/3343413.3377987

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Note that we pre-process all intents by removing stopwords and conducting lemmatization. The results show that our task allows to collect a diverse set of intent patterns; in comparison, related studies such as [8] only consider two intent patterns, activity (verb) and audience (noun). A summary of the statistics of our collected datasets is given in Table 2.…”

Section: Resulting Datasetsmentioning

confidence: 90%

“…We sample 2000 sessions for each domain with lengths from 2 to 10 considering that (1) most session lengths are in the range of [2,10] (see Figure 3(a)); and that (2) longer sessions may contain noisy items and increase the difficulty of annotation. To account for the imbalance of the session length, we adopt the stratified sampling strategy: for each domain, we divide the sessions into 4 groups based on their lengths -[2, 3], [4,5], [6,7], [8,9,10], and then sample 500 sessions from each group. As a result, we obtain 2000 sessions with balanced length distribution from each domain.…”

Section: Crowdsourced Annotation Taskmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting Bundle Recommendation

Sun

Yang

Feng

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (https://github.com/BundleRec/bundle_recommendation).

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Section: Resulting Datasetsmentioning

confidence: 90%

Section: Crowdsourced Annotation Taskmentioning

confidence: 99%

Revisiting Bundle Recommendation

Sun

Yang

Feng

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

show abstract

“…Transactional queries are identified by CURL in [10]. Authors in [11] trained a classifier to label queries with the intents extracted from user reviews. In [12] query-specific features, such as bag-of-words, length, recognized named entity, noun phrase, question and so on, are exploited to build three multi-class classifiers.…”

Section: Related Workmentioning

confidence: 99%

Query Intent Detection from the SEO Perspective

Mohammadi¹,

Chapon²,

Fremond³

2020

Preprint

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Google users have different intents from their queries such as acquiring information, buying products, comparing or simulating services, looking for products and so on. Understanding the right intention of users helps to provide i) better content on web pages from the Search Engine Optimization (SEO 1 ) perspective and ii) more user-satisfying results from the search engine perspective. In this study, we aim to identify the user query's intent by taking advantage of Google results and machine learning methods. Our proposed approach is a clustering model that exploits some features to detect query's intent. A list of keywords extracted from the clustered queries is used to identify the intent of a new given query. Comparing the clustering results with the intents predicted by filtered keywords show the efficiency of the extracted keywords for detecting intents.

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