Supporting Top-K Item Exchange Recommendation in Large Online Community
Zhan Su, Anthony K. H. Tung, Zhenjie Zhang
Item Exchange: Pioneer Trail / Frontier Ville • The most popular social game on Facebook
Item Exchange: Pioneer Trail / Frontier Ville • Player needs items to finish tasks
Item Exchange: Pioneer Trail / Frontier Ville • Wish List: waiting for help from your friends
Item Exchange: World of Warcraft • Auction-based approach
Item Exchange: World of Warlord • Problems with Auction-based approach – Inflation: the players prefer to exchange instead of selling their weapons
Item Exchange: Real Item • A popular website in Chinese
Item Exchange: Service • Some item/service swap web sites: – http://www.swapcycle.co.uk/ – http://www.u-exchange.com/
Agenda of the presentation • • • • •
Problem Definition (5 mins) Exchange between 2 users (5 mins) General Recommendation (5 mins) Experiments (5 mins) Conclusion and Future Work (2 mins)
Exchange Model • Exchange Model Wish List Unneeded List
Price List
$1
$5
Wish List
$2
$10
Unneeded List
$8
$16
Wish List Unneeded List
Eligible Exchange Pair • Exchange Model Wish List Unneeded List
Price List
$1
$5
Wish List
$2
$10
Unneeded List
$8
$16
Wish List Unneeded List
Eligible Exchange Pair • Formal definition – Only between two users – The unneeded items are in the wish list of the other user (item matching) – The values of the exchange items are approximately the same. Specifically, the ratio between the value is between b and b-1. b=0.8
+
$16
$1
$18
$10
Top-K Exchange Recommendation • How to maximize the utility of the exchange – We aim to recommend exchange candidates with maximal value for each user, i.e. sum on the item prices – For Alice, the value of the exchange is $15. For Bob, the value of the exchange is $16
$16
+
$18
Top-K Exchange Recommendation • How to maximize the utility of the exchange – There’s no multiple exchange pairs on a single pair of user. Why? – We prove that there is always a dominating exchange pair, maximizing the utilities of both sides.
$16
+
$18
Research Target • System’s Perspective – Handling updates on the list in real time – High throughput of the system – Scalability of the system in terms of users Wish List Unneeded List Wish List Unneeded List
Price List
1$
5$
2$
10$
8$
16$
We’re only … • Recommendation only – There’s no automatic commitment of the exchange
• Exchange between 2 users only – Multi-party exchange chain is hard to find and even harder to proceed
• No currency used – Inflation issue
Agenda of the presentation • • • • •
Problem Definition (5 mins) Exchange between 2 users (5 mins) General Recommendation (5 mins) Experiments (5 mins) Conclusion and Future Work (2 mins)
Hardness result • NP-hardness on finding optimal exchange – In terms of the lengths of the lists – Polynomial reduction from Load Balancing
• Fortunately, – The number of items are usually bounded by a constant, e.g. every individual player in WoW has limited number of weapons in hand.
Polynomial-Time Approximation Scheme • Map the combinations onto the price axis $8
$2
Wish List Unneeded List Wish List Unneeded List
$10
$16
$18
Price List
$1
$5
$2
$10
$8
$16
Approximate Value Table • Join the Wish list of Bob and Unneeded list of Celina Wish List Unneeded List Wish List Unneeded List
• Approximate all combinations using Approximate Value Table (AVT)
Approximate Value Table Iteration 1: Paper ($2) $1 $2 $3 $4
$6
$9
Entry App. Value LB
$13
$19
LB Items
UB
$28
UB Items
Approximate Value Table Iteration 1: Paper ($2) {Paper} is added $1 $2 $3 $4
$6
$9
$13
$19
$28
Entry App. Value LB
LB Items
UB
UB Items
1
{Paper}
2
{Paper}
2
2
Approximate Value Table Iteration 2: Bike ($16) $1 $2 $3 $4
$6
$9
$13
$19
$28
Entry App. Value LB
LB Items
UB
UB Items
1
{Paper}
2
{Paper}
2
2
Approximate Value Table Iteration 2: Bike ($16) {Bike} is added $1 $2 $3 $4
$6
$9
$13
$28
$19
Entry App. Value LB
LB Items
UB
UB Items
1
2
2
{Paper}
2
{Paper}
2
19
16
{Bike}
16
{Bike}
Approximate Value Table Iteration 2: Bike ($16) {Paper, Bike} is added $1 $2 $3 $4
$6
$9
$13
$19
$28
Entry App. Value LB
LB Items
UB
UB Items
1
2
2
{Paper}
2
{Paper}
2
19
16
{Bike}
18
{Paper, Bike}
Approximate Value Table Entry App. Value LB
LB Items
UB
UB Items
1
9
8
{Hammer}
8
{Hammer}
2
13
10
{Ribbon}
10
{Ribbon}
3
19
18
{Hammer, Ribbon}
18
{Hammer, Ribbon}
Entry App. Value LB
LB Items
UB
UB Items
1
2
2
{Paper}
2
{Paper}
2
19
16
{Bike}
18
{Paper, Bike}
Linear scan the UB of one table
AVT v.s. Brute-Force • AVT is more effective when list length>8
Agenda of the presentation • • • • •
Problem Definition (5 mins) Exchange between 2 users (5 mins) General Recommendation (5 mins) Experiments (5 mins) Conclusion and Future Work (2 mins)
A Naïve Solution • Maintain two AVTs between every pair of user – Quadratic overhead on storage – Linear update cost in terms of user #
• How to improve? How to narrow down the search space? – Critical Item – Optimizations on Insertion – Optimizations on Deletion
Critical Item • Some items are necessary for eligible exchange – If you don’t have hammer and ribbon, you can never exchange with Celina – If you don’t want bicycle, you can never exchange with Celina Wish List Unneeded List
Insertion • When a new item is inserted into a list – Update critical items for the user. Stop if new item is not critical – Find the users with the critical items – Recalculate the optimal exchange between Alice and every candidate user Wish List Unneeded List Wish List Unneeded List
Not critical!
Deletion • Can we update the recommendation only? – No. It may affect the top-k recommendation of others
Wish List Unneeded List Wish List Unneeded List
Wish List Unneeded List
Deletion • To keep all recommendations optimal – Update every pair of user with valid critical items – Much more expensive than insertion
• How to avoid unnecessary re-calculation? – Maintain k’ > k pairs of optimal recommendations – When some recommendations disappear, just display the backup ones
Agenda of the presentation • • • • •
Problem Definition (5 mins) Exchange between 2 users (5 mins) General Recommendation (5 mins) Experiments (5 mins) Conclusion and Future Work (2 mins)
Simulation Setup • Basic parameters – – – – – –
Maximal Value: $10,000 Minimal Value: $10 Matching relaxation b: 0.8 (default) List length: 15 (default) Number of items: 1500 (default) Number of users: 30,000 (default)
• Item Distribution – Synthetic data: exponential distribution, Zipf distribution – Real data: Ebay auction data
Simulation Setup • Item/Price Distribution
Workload Generation • Workload: a sequence of updates – Initially, the database is empty – Uniform selection on the user – Pick up Wanted list or Unneeded list with equal probability – Pick up insertion or deletion operation – Randomly pick an item based on a specific distribution on the items – Add/Remove the item in the specific list
Dynamics of the Simulation • System is stable after the lists are almost full
Results on Simulation with Ebay data
Conclusion • Item exchange: an emerging business of huge market value • Top-K Item exchange problem • Database performance issue • General system engine to support updates
Future Work • Social Network • Item Priority • System Performance – Index Structure: bitmap – Transaction: Insertion of item groups
Supporting Top-K Item Exchange Recommendation in ...
... Pioneer Trail / Frontier Ville. ⢠The most popular social game on Facebook ... A popular website in Chinese ... List. $1. $2. $8. $5. $10. $16. Price List. Wish. List. Unneeded. List. Wish. List. Unneeded. List .... Social Network. ⢠Item Priority.
School of Computing ... the items for exchange in online games are usually virtual objects, ..... list Wi for ui and unneeded item list Lj . A new item In+1 is.
set Bi ⪠{vj} must occur in at least some minimum number .... user ui and a basket Bi, we construct a recommendation list of target ..... Response Time (ms). FM.
Fei Wang. Department of Automation ... recommender system - a personalized information filtering ... Various approaches for recommender systems have been.
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