Promote Product Reviews of High Quality on Ecommerce Sites
Notice bibliographique
Résumé
With the community of online reviewers growing rapidly, we find it increasingly difficult to digest all the information within a limited time. Users’ requirements raise an interesting problem not well studied yet: how to discover the high quality product reviews? We believe a good solution will provide at least two types of benefit: 1) Rank reviews in terms of their quality. This could improve user experience by enabling them to learn more with a few detailed high-quality reviews instead of review outlines of irrelevant content and spam. 2) Automatically summarize user opinions. Researchers have studied this problem for years and are trying to assist users in getting the main products information concepts more efficiently. With this respect, low-quality content will definitely degrade the accuracy performance of any algorithm on this task. For the purpose of quality prediction, previous research thoroughly examined various properties of product reviews based on their content. Although some promising results have been obtained, we believe there is still room for improvement. Overall, we explore the topic of review quality from two aspects: 1) to filter out noisy data. Here we leverage classification techniques to differentiate real product reviews from other types of reviews and spam. Indeed many articles that fall under the label “product reviews” really belong to three groups: product reviews, feedback for retailers, and commercial spam. The empirical results show that this research could be put into practice with sufficient training data. 2) To assess the quality of a review we also take into consideration another information resource: the behavior of a review author in an e-commerce community. Our requirement is that after the noise filtering step, all product reviews must be ranked according to their quality. The common methods for this type of task are usually based solely on the analysis of the text of the review. By contrast, we performed a high-level analysis on two kinds of data: product reviews and deal transactions. An interesting finding reveals that review quality is not only related to their content, but can also be derived from the behavior of the review author. Therefore, in order to inspect review quality from the perspectives of human credibility and expertise, we consider the following three features: the author personal reputation, the “seller degree” that reflects if the author is also a seller, and the “expertise degree”. Our experiments show that the addition of these features increase the performance
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».