Bibliographic record
Abstract
Most of the recommender systems are built for the content or item providers. For example, Netflix recommends movies or TV shows, Amazon recommends books or other items being sold on Amazon, Facebook or Twitter recommends popular posts or tweets, Pinterest recommends related pins, YouTube recommends videos, etc. Most of these recommender systems are designed based on the usage data collected on their own websites. However, sometimes it could be helpful if we could get information about recommended items from multiple data sources, providing multiple perspectives for users to make their decisions. In this research work, we study different approaches to integrate multiple data sources and the effect on the recommendation results when multiple data sources are used to recommend items. We propose a multiple data source-based movie recommender system that uses MovieLens rating data, YouTube movie trailer data, Netflix rating data, and tweets from Twitter. The user feedback data such as likes, dislikes, comments on movie trailers posted on YouTube can be helpful side information for movie recommender systems. In this research work, we study the effect of adding these side information to the movie rating data. Our proposed recommendation framework can integrate the trailer and rating data adopting various integration strategies: integrating all the trailer data as movie features, using sentiment scores derived from the trailer comments as a rating matrix to integrate with the movie rating matrix, and treating others as the movie features, or only integrating the sentiment score based rating matrix with the movie rating matrix. Our experiment shows that if we include the movie trailer data, recommendation accuracy is improved. We also find that the most accurate result is achieved if all the trailer feedback data is integrated as movie features. We use both Matrix Factorization (MF) and Deep Neural Network (DNN) Models to design our system. We find that the DNN model performs better than the MF model. Our results show that when we include multiple data sources to recommend items using the DNN model, the recommendation accuracy (F1 score) is increased by 41% on average comparing to the case when only one data source is used.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".