官术网_书友最值得收藏!

Content-based systems

Unlike collaborative filters, content-based systems do not require data relating to past activity. Instead, they provide recommendations based on a user profile and metadata it has on particular items.

Netflix is an excellent example of the aforementioned system. The first time you sign in to Netflix, it doesn't know what your likes and dislikes are, so it is not in a position to find users similar to you and recommend the movies and shows they have liked. 

As shown in the previous screenshot, what Netflix does instead is ask you to rate a few movies that you have watched before. Based on this information and the metadata it already has on movies, it creates a watchlist for you. For instance, if you enjoyed the Harry Potter and Narnia movies, the content-based system can identify that you like movies based on fantasy novels and will recommend a movie such as Lord of the Rings to you.

However, since content-based systems don't leverage the power of the community, they often come up with results that are not as impressive or relevant as the ones offered by collaborative filters. In other words, content-based systems usually provide recommendations that are obvious. There is little novelty in a Lord of the Rings recommendation if Harry Potter is your favorite movie. 

主站蜘蛛池模板: 清河县| 都昌县| 通渭县| 大兴区| 望奎县| 酒泉市| 宁津县| 呼玛县| 嘉禾县| 白河县| 策勒县| 克什克腾旗| 东乡| 淳安县| 木里| 当涂县| 九江市| 齐齐哈尔市| 沅江市| 聂荣县| 沭阳县| 新绛县| 类乌齐县| 日照市| 福鼎市| 宝兴县| 运城市| 淮滨县| 梁平县| 阜阳市| 醴陵市| 镇赉县| 黎川县| 弥勒县| 平和县| 赣榆县| 新宁县| 宁蒗| 札达县| 方山县| 乌拉特前旗|