Item-based recommendation

User-based: Who is similar to the boy, and what do they like?

Item-based:  What is similar to what the boy likes?

 

The algorithm

 The difference between User-based and Item-based :

Slope-one recommender 

 It estimates preferences for new items based on average difference in the preference value (diffs) between a new item and the other items the user prefers.

Formula

 

 

In this case, the average difference in ratings between item B and A is (2+(-1))/2=0.5. Hence, on average, item A is rated above item B by 0.5. Similarly, the average difference between item C and A is 3. Hence, if we attempt to predict the rating of Lucy for item A using her rating for item B, we get 2+0.5 = 2.5. Similarly, if we try to predict her rating for item A using her rating of item C, we get 5+3=8.

If a user rated several items, the predictions are simply combined using a weighted average where a good choice for the weight is the number of users having rated both items. In the above example, we would predict the following rating for Lucy on item A:

Hence, given n items, to implement Slope One, all that is needed is to compute and store the average differences and the number of common ratings for each of the n² pairs of items.

 

 

preprocessing phase, in which all item-item preference value differences are computed:

 

recommendation algorithm looks like this:

 

SampleCode

DiffStorage diffStorage = new MemoryDiffStorage(model, Weighting.UNWEIGHTED, Long.MAX_VALUE));
return new SlopeOneRecommender(model,Weighting.UNWEIGHTED,Weighting.UNWEIGHTED,
diffStorage); 
//or 
return new SlopeOneRecommender(model)

 Slope-one does have its price: memory consumption.It may become necessary to store diffs elsewhere.

AbstractJDBCDataModel model = new MySQLJDBCDataModel();
DiffStorage diffStorage = new MySQLJDBCDiffStorage(model);
Recommender recommender = new SlopeOneRecommender(model, Weighting.WEIGHTED, Weighting.WEIGHTED, diffStorage);

 

 

 

References

http://en.wikipedia.org/wiki/Slope_One