Personalized and Explainable Aspect-based Recommendation using Latent Opinion Groups
Bibliographic record
Abstract
The problem of explainable recommendation—supporting the recommendation of a product or service with an explanation of why the item is a good choice for the user—is attracting substantial research attention recently. Recommendations associated with an explanation of how the aspects of the chosen item may meet the needs and preferences of the user can improve the transparency and trustworthiness of consumer-oriented applications, which is the motivation driving this research area. Current methods are far from ideal because they do not necessarily consider the following issues: users’ opinions are influenced not only by individual aspects but also by the dependency between sentiments towards aspect; not all users place the same value on all aspects; and, any explanation are not provided for how the item aspects have led to the recommendation. We introduce a personalized explainable aspect-based recommendation method that can address these challenges. To identify the aspects that a user cares about, our semantics-aware method learns the likelihood of an aspect being mentioned in a user’s review. To capture dependency between the users’ sentiments towards an aspect, reviews that express opinions with similar polarities towards sets of aspects are clustered together in latent opinion groups. To construct aspect-based explanations, item aspects are rated according to their importance based on these latent opinion groups and the preferences of the target user. Finally, to provide a user with a (set of) useful recommendation(s) of an item, our method selects and synthesizes the aspects important for the target user. We evaluate our method over two datasets from (a) Yelp and (b) Tripadvisor. Our results demonstrate that our method outperforms previous methods in both recommendation performance and explainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".