AR

Simple Association Rules (AR) are a simplified version of the association rule mining technique [Agrawal et al. 1993] with a maximum rule size of two. The method is designed to capture the frequency of two co-occurring events, e.g., “Customers who bought . . . also bought”.

research paper

Agrawal et. al., “Mining Association Rules Between Sets of Items in Large Databases”. SIGMOD, 1993.

Algorithmically, the rules and their corresponding importance are “learned” by counting how often the items i and j occurred together in a session of any user. Let a session s be a chronologically ordered tuple of item click events s = ($s_1$,$s_2$,$s_3$, . . . ,$s_m$) and $S_p$ the set of all past sessions. Given a user’s current session s with $s_{|s|}$ being the last item interaction in s, we can define the score for a recommendable item i as follows, where the indicator function $1_{EQ}(a,b)$ is 1 in case a and b refer to the same item and 0 otherwise.

$$score_{AR}(i,s) = \dfrac{1}{\sum_{p \in S_p}\sum_{x=1}^{|p|}1_{EQ}(s_{|s|},p_x)\cdot(|p|-1)}\sum_{p \in s_p}\sum_{x=1}^{|p|}\sum_{y=1}^{|p|}1_{EQ}(s_{|s|},p_x)\cdot1_{EQ}(i,p_y)$$

In the above equation, the sums at the right-hand side represent the counting scheme. The term at the left-hand side normalizes the score by the number of total rule occurrences originating from the current item $s_{|s|}$. A list of recommendations returned by the ar method then contains the items with the highest scores in descending order. No minimum support or confidence thresholds are applied.

References

• https://arxiv.org/pdf/1803.09587.pdf
• https://dl.acm.org/doi/10.1145/170036.170072
• http://www.rakesh.agrawal-family.com/papers/sigmod93assoc.pdf
• https://github.com/mmaher22/iCV-SBR/tree/master/Source Codes/AR%26SR_Python