Slope One implementations offers poor recommendations

Question

I'm attempting to implement a Slope One algorithm via PHP for user-based item recommendation. To do this, I'm using the OpenSlopeOne library. The problem I'm having is that the recommendations generated aren't at all relevant to the user.

Currently I have two tables: user_ratings and slope_one. The user_ratings table is fairly straight forward. It contains a per-item rating given by that particular user (user_id, item_id and user_item_rating). The slope_one table follows OpenSlopeOne's default schema: item_id1, item_id2, times and rating.

The slope_one table is populated using the following SQL procedure:

CREATE PROCEDURE `slope_one`()
begin                    
    DECLARE tmp_item_id int;
    DECLARE done int default 0;                    
    DECLARE mycursor CURSOR FOR select distinct item_id from user_ratings;
    DECLARE CONTINUE HANDLER FOR NOT FOUND set done=1;
    open mycursor;
    while (!done) do
        fetch mycursor into tmp_item_id;
        if (!done) then
            insert into slope_one (select a.item_id as item_id1,b.item_id as item_id2,count(*) as times, sum(a.rating-b.rating) as rating from user_ratings a, user_ratings b where a.item_id = tmp_item_id and b.item_id != a.item_id and a.user_id=b.user_id group by a.item_id,b.item_id);
        end if;
    END while;
    close mycursor;
end

And to fetch the most relevant recommendations for a given user, I perform the following query:

SELECT
    item.* 
FROM
    slope_one s,
    user_ratings u,
    item
WHERE 
    u.user_id = '{USER_ID}' AND 
    s.item_id1 = u.item_id AND 
    s.item_id2 != u.item_id AND
    item.id = s.item_id2
GROUP BY 
    s.item_id2 
ORDER BY
    SUM(u.rating * s.times - s.rating) / SUM(s.times) DESC
LIMIT 20

As previously stated, this just doesn't seem to be working. I'm working with a fairly large data set (10,000+ recommendations) but I'm just not seeing any form of correlation. In fact, the majority of recommendations seem to be identical for users, even with totally disparate item ratings.

Answer 1

You could try the Java implementation in Apache Mahout. There is an excerpt from Mahout in Action which covers its usage. That might be useful as a second data point and help differentiate algorithm versus implementation issues.

As of Mahout 0.9 the remmenders are discontinued. See https://mahout.apache.org/