Abstract: Systems and methods that analyze aggregated item evaluation behavior of users, to suggest a recommendation for the item. An analysis component forms a collective opinion by taking as input votes of users and trusted relationships established therebetween, to output an evaluation and/or recommendation for the item. Accordingly, within a linked structure of nodes, personalized recommendations to users (e.g., agents) are supplied about an item(s) based upon the opinions/reviews of other users, and in conjunction with the declared trust between the users.

Abstract: Systems and methods that analyze aggregated item evaluation behavior of users, to suggest a recommendation for the item. An analysis component forms a collective opinion by taking as input votes of users and trusted relationships established therebetween, to output an evaluation and/or recommendation for the item. Accordingly, within a linked structure of nodes, personalized recommendations to users (e.g., agents) are supplied about an item(s) based upon the opinions/reviews of other users, and in conjunction with the declared trust between the users.

Abstract: Hash tables comprising load factors of up to and above 97% are disclosed. The hash tables may be associated with three or more hash functions, each hash function being applied to a key to identify a location in a hash table. The load factor of a hash table may be increased, obviating any need to increase the size of the hash table to accommodate more insertions. Such increase in load factor may be accomplished by a combination of increasing the number of cells per bucket in a hash table and increasing the number of hash functions associated with the hash table.