Recommendation for further research
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Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique. User feedback is used to refine the station’s results, deemphasizing certain attributes when a user «dislikes» a particular song and emphasizing other attributes when a user «likes» a song. This is an example of a content-based approach.
Each type of system has its strengths and weaknesses. This is an example of the cold start problem, and is common in collaborative filtering systems. Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise. Of note, recommender systems are often implemented using search engines indexing non-traditional data.
Montaner provided the first overview research recommender for from an intelligent agent perspective. Adomavicius provided a new, alternate overview of recommender systems. Recommender systems have been the focus of several granted patents. One approach to further design of recommender systems that has wide use is recommendation filtering.
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Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past. When building a model from a user’s behavior, a distinction is often made between explicit and implicit forms of data collection. Asking a user to rate an item on a sliding scale.
Asking a user to rank a collection of items from favorite to least favorite. Observing the items that a user views in an online store. Keeping a record of the items that a user purchases online. Analyzing the user’s social network and discovering similar likes and dislikes. The recommender system compares the collected data to similar and dissimilar data collected from others and calculates a list of recommended items for the user.
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