## Recommender System: Recommendation Algorithms

5th July 2017 at 10:07am
Recommender System
• Non-Personalized Summary Statistics
• Mean-based:
• Background: In a 5-star scale rating system
• Symbols:
• $U_i$ for users who rated item $i$
• $r_{ui}$ for rating by user $u$ to item $i$
• $|U_i|$ for number of user who rated item $i$
• $\alpha$ for damping factor, larger would cause the rating floats more smooth
• $\mu$ for global rating across all items and users
• $s(i)$ for score of item $i$
• Mean: $s(i) = \dfrac{\sum_{u \in U_i}^n r_{ui}}{|U_i|}$
• Damped Mean: $s(i) = \dfrac{\sum_{u \in U_i}^n r_{ui} + \alpha\mu}{|U_i| + \alpha}$
• Association-based:
• Background: typically in a shop that customs buying products
• Symbols:
• $P(i|j)$: probability of buying i when already buying j
• Basic Association, measuring probability by counting:
• Story: How many percentage of people who buying i also buying j among the whole people who buying i?
• Formula: $P(i|j) = \dfrac{P(i \land j)}{P(j)} = \dfrac{|U_i \cap U_j| / |U|}{|U_j| / |U|}$
• Bad case: if j is popular, then the result is bad
• Bayes's Law: $P(i|j) = \dfrac{P(j|i) P(i)}{P(j)}$
• Lift Association, measuring score by counting:
• Story: people who bought i and j together more often means i and j are more associative
• Formula: $s(i|j) = \dfrac{P(j \land i)}{P(i) P(j)}$
• Content-Based Filtering
• Information Filtering
• Knowledge-Based
• Collaborative Filtering
• User-User
• Item-Item
• Dimensionality Reduction
• Others
• Critique / Interview Based Recommendations
• Hybrid Techniques