| 摘要 |
A suggestion server generates suggestions of videos. The suggestion server analyzes log data to create co-watch data identifying pairs of co-watched videos and containing generate values representing the number of times the pairs of videos were co-watched. The suggestion server uses the co-watch data to create feature vectors for the co-watched videos. The suggestion server uses the feature vectors to train a ranker for each video. When trained, the ranker can be applied to a feature vector for a video to produce a ranking score. To produce suggestions for a given video, a set of candidate videos is defined. The suggestion server applies the feature vectors for the candidates to the ranker for the given video to produce ranking scores. The candidate videos are ranked based on their ranking scores, and the highest-ranked candidates are provided as suggestions for the given video. |
