20241007 - RS - Content-based Filtering - Part 2

The video discussed improving the factor space model, utilizing lexical knowledge, calculating similarity between factors, and using nearest neighbor analysis for predicting ratings and recommendations.
The factor space model can be improved by removing stop words, using stemming or lemmatization, and handling phrases.
Lexical knowledge is important for understanding the relevance of words and detecting lexical ambiguity.
The TF-IDF can be used to calculate similarity between factors and adjusted ratings can be used for recommendation.
Nearest neighbor analysis can be used to find similar documents and predict ratings.
Item I will be liked by the current user if four of the K nearest items were liked.
Alice will most likely like item five based on similarity with three other items.
The number of nearest neighbors (K) can vary and affect the accuracy of predictions.