《海量数据挖掘-王永利》ch09-recsys1.pptVIP

* * * * * * * -- Jaccard is not appropriate as we want to consider weights * * * There is a difference in the typical behavior of users and items, as it pertains to similarity. Intuitively, items tend to be classifiable in simple terms. For example, music tends to belong to a single genre. It is impossi- ble, e.g., for a piece of music to be both 60’s rock and 1700’s baroque. On the other hand, there are individuals who like both 60’s rock and 1700’s baroque, and who buy examples of both types of music. The consequence is that it is easier to discover items that are similar because they belong to the same genre, than it is to detect that two users are similar because they prefer one genre in common, while each also likes some genres that the other doesn’t care for. * * * * * * J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, * rx = [*, _, _, *, ***] ry = [*, _, **, **, _] rx, ry as sets: rx = {1, 4, 5} ry = {1, 3, 4} rx, ry as points: rx = {1, 0, 0, 1, 3} ry = {1, 0, 2, 2, 0} rx, ry … avg.rating of x, y Intuitively we want: sim(A, B) sim(A, C) Jaccard similarity: 1/5 2/4 Cosine similarity: 0.386 0.322 Considers missing ratings as “negative” Solution: subtract the (row) mean J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, * sim A,B vs. A,C: 0.092 -0.559 Notice cosine sim. is correlation when data is centered at 0 Cosine sim: * J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, So far: User-user collaborative filtering Another view: Item-item For item i, find other similar items Estimate rating for item i based on ratings for similar items Can use same similarity metrics and prediction functions as in user-user model J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, * sij… similarity of items i and j rxj…rating of user u on item j N(i;x)… set items rated by x similar to i 12 11 10 9 8 7 6 5 4 3 2 1 4 5 5 3 1 1 3 1 2 4 4 5 2 5 3 4 3 2 1 4 2 3 2 4 5 4 2 4 5 2 2 4 3 4 5 4 2 3 3 1 6 users movi