best-selling items, the algorithm typically multi- Unfortunately, all these methods also reduce
plies the vector components by the inverse fre- recommendation quality in several ways. First, if
quency (the inverse of the number of customers the algorithm examines only a small customer
who have purchased or rated the item), making less sample, the selected customers will be less similar
well-known items much more relevant.3 For almost to the user. Second, item-space partitioning
all customers, this vector is extremely sparse. restricts recommendations to a specific product or
The algorithm generates recommendations subject area. Third, if the algorithm discards the
based on a few customers who are most similar to most popular or unpopular items, they will never
the user. It can measure the similarity of two cus- appear as recommendations, and customers who
tomers, A and B, in various ways; a common have purchased only those items will not get rec-
method is to measure the cosine of the angle ommendations. Dimensionality reduction tech-
between the two vectors: 4 niques applied to the item space tend to have the
same effect by eliminating low-frequency items.
r r Dimensionality reduction applied to the customer
r r r r A•B
similarity A( ) ( )
, B = cos A, B = r r
A * B
space effectively groups similar customers into
clusters; as we now describe, such clustering can
also degrade recommendation quality.
The algorithm can select recommendations from Cluster Models
the similar customers’ items using various meth- To find customers who are similar to the user, clus-
ods as well, a common technique is to rank each ter models divide the customer base into many seg-
item according to how many similar customers ments and treat the task as a classification problem.
purchased it. The algorithm’s goal is to assign the user to the seg-
Using collaborative filtering to generate recom- ment containing the most similar customers. It then
mendations is computationally expensive. It is uses the purchases and ratings of the customers in
O(MN) in the worst case, where M is the number the segment to generate recommendations.
of customers and N is the number of product cat- The segments typically are created using a clus-
alog items, since it examines M customers and up tering or other unsupervised learning algorithm,
to N items for each customer. However, because although some applications use manually deter-
the average customer vector is extremely sparse, mined segments. Using a similarity metric, a clus-
the algorithm’s performance tends to be closer to tering algorithm groups the most similar customers
O(M + N). Scanning every customer is approxi- together to form clusters or segments. Because
mately O(M), not O(MN), because almost all cus- optimal clustering over large data sets is imprac-
tomer vectors contain a small number of items, tical, most applications use various forms of
regardless of the size of the catalog. But there are greedy cluster generation. These algorithms typi-
a few customers who have purchased or rated a cally start with an initial set of segments, which
significant percentage of the catalog, requiring often contain one randomly selected customer
O(N) processing time. Thus, the final performance each. They then repeatedly match customers to the
of the algorithm is approximately O(M + N). Even existing segments, usually with some provision for
so, for very large data sets — such as 10 million or creating new or merging existing segments.6 For
more customers and 1 million or more catalog very large data sets — especially those with high
items — the algorithm encounters severe perfor- dimensionality — sampling or dimensionality
mance and scaling issues. reduction is also necessary.
It is possible to partially address these scaling Once the algorithm generates the segments, it
issues by reducing the data size.4 We can reduce M computes the user’s similarity to vectors that sum-
by randomly sampling the customers or discarding marize each segment, then chooses the segment
customers with few purchases, and reduce N by dis- with the strongest similarity and classifies the user
carding very popular or unpopular items. It is also accordingly. Some algorithms classify users into
possible to reduce the number of items examined multiple segments and describe the strength of
by a small, constant factor by partitioning the item each relationship.7
space based on product category or subject classi- Cluster models have better online scalability
fication. Dimensionality reduction techniques such and performance than collaborative filtering3
as clustering and principal component analysis can because they compare the user to a controlled
reduce M or N by a large factor.5 number of segments rather than the entire cus-
IEEE INTERNET COMPUTING http://computer.org/internet/ JANUARY • FEBRUARY 2003 77
form well. For users with thousands of purchases,
however, it’s impractical to base a query on all the
items. The algorithm must use a subset or summa-
ry of the data, reducing quality. In all cases, rec-
ommendation quality is relatively poor. The rec-
ommendations are often either too general (such
as best-selling drama DVD titles) or too narrow
(such as all books by the same author). Recom-
mendations should help a customer find and dis-
Figure 1. The “Your Recommendations” feature on the Amazon.com cover new, relevant, and interesting items. Popu-
homepage. Using this feature, customers can sort recommendations lar items by the same author or in the same subject
and add their own product ratings. category fail to achieve this goal.
Amazon.com uses recommendations as a targeted
marketing tool in many email campaigns and on
most of its Web sites’ pages, including the high-
traffic Amazon.com homepage. Clicking on the
“Your Recommendations” link leads customers to an
Figure 2. Amazon.com shopping cart recommendations. The recom- area where they can filter their recommendations by
mendations are based on the items in the customer’s cart: The product line and subject area, rate the recommended
Pragmatic Programmer and Physics for Game Developers. products, rate their previous purchases, and see why
items are recommended (see Figure 1).
As Figure 2 shows, our shopping cart recom-
tomer base. The complex and expensive clustering mendations, which offer customers product sug-
computation is run offline. However, recommen- gestions based on the items in their shopping cart.
dation quality is low.1 Cluster models group The feature is similar to the impulse items in a
numerous customers together in a segment, match supermarket checkout line, but our impulse items
a user to a segment, and then consider all cus- are targeted to each customer.
tomers in the segment similar customers for the Amazon.com extensively uses recommendation
purpose of making recommendations. Because the algorithms to personalize its Web site to each cus-
similar customers that the cluster models find are tomer’s interests. Because existing recommendation
not the most similar customers, the recommenda- algorithms cannot scale to Amazon.com’s tens of
tions they produce are less relevant. It is possible millions of customers and products, we developed
to improve quality by using numerous fine- our own. Our algorithm, item-to-item collaborative
grained segments, but then online user–segment filtering, scales to massive data sets and produces
classification becomes almost as expensive as find- high-quality recommendations in real time.
ing similar customers using collaborative filtering.
How It Works
Search-Based Methods Rather than matching the user to similar cus-
Search- or content-based methods treat the rec- tomers, item-to-item collaborative filtering match-
ommendations problem as a search for related es each of the user’s purchased and rated items to
items.8 Given the user’s purchased and rated similar items, then combines those similar items
items, the algorithm constructs a search query to into a recommendation list.9
find other popular items by the same author, To determine the most-similar match for a given
artist, or director, or with similar keywords or item, the algorithm builds a similar-items table by
subjects. If a customer buys the Godfather DVD finding items that customers tend to purchase
Collection, for example, the system might recom- together. We could build a product-to-product
mend other crime drama titles, other titles star- matrix by iterating through all item pairs and com-
ring Marlon Brando, or other movies directed by puting a similarity metric for each pair. However,
Francis Ford Coppola. many product pairs have no common customers,
If the user has few purchases or ratings, search- and thus the approach is inefficient in terms of
based recommendation algorithms scale and per- processing time and memory usage. The following
78 JANUARY • FEBRUARY 2003 http://computer.org/internet/ IEEE INTERNET COMPUTING
iterative algorithm provides a better approach by large data sets, unless it uses dimensionality
calculating the similarity between a single prod- reduction, sampling, or partitioning — all of
uct and all related products: which reduce recommendation quality.
• Cluster models can perform much of the com-
For each item in product catalog, I1 putation offline, but recommendation quality
For each customer C who purchased I1 is relatively poor. To improve it, it’s possible to
For each item I2 purchased by increase the number of segments, but this
customer C makes the online user–segment classification
Record that a customer purchased I1 expensive.
and I2 • Search-based models build keyword, category,
For each item I2 and author indexes offline, but fail to provide
Compute the similarity between I1 and I2 recommendations with interesting, targeted
titles. They also scale poorly for customers with
It’s possible to compute the similarity between two numerous purchases and ratings.
items in various ways, but a common method is to
use the cosine measure we described earlier, in which The key to item-to-item collaborative filtering’s
each vector corresponds to an item rather than a scalability and performance is that it creates the
customer, and the vector’s M dimensions correspond expensive similar-items table offline. The algo-
to customers who have purchased that item. rithm’s online component — looking up similar
This offline computation of the similar-items items for the user’s purchases and ratings — scales
table is extremely time intensive, with O(N2M) as independently of the catalog size or the total num-
worst case. In practice, however, it’s closer to ber of customers; it is dependent only on how
O(NM), as most customers have very few purchas- many titles the user has purchased or rated. Thus,
es. Sampling customers who purchase best-selling the algorithm is fast even for extremely large data
titles reduces runtime even further, with little sets. Because the algorithm recommends highly
reduction in quality. correlated similar items, recommendation quality
Given a similar-items table, the algorithm finds is excellent.10 Unlike traditional collaborative fil-
items similar to each of the user’s purchases and tering, the algorithm also performs well with lim-
ratings, aggregates those items, and then recom- ited user data, producing high-quality recommen-
mends the most popular or correlated items. This dations based on as few as two or three items.
computation is very quick, depending only on the
number of items the user purchased or rated. Conclusion
Recommendation algorithms provide an effective
Scalability: A Comparison form of targeted marketing by creating a person-
Amazon.com has more than 29 million customers alized shopping experience for each customer. For
and several million catalog items. Other major large retailers like Amazon.com, a good recom-
retailers have comparably large data sources. mendation algorithm is scalable over very large
While all this data offers opportunity, it’s also a customer bases and product catalogs, requires only
curse, breaking the backs of algorithms designed subsecond processing time to generate online rec-
for data sets three orders of magnitude smaller. ommendations, is able to react immediately to
Almost all existing algorithms were evaluated over changes in a user’s data, and makes compelling
small data sets. For example, the MovieLens data recommendations for all users regardless of the
set4 contains 35,000 customers and 3,000 items, number of purchases and ratings. Unlike other
and the EachMovie data set3 contains 4,000 cus- algorithms, item-to-item collaborative filtering is
tomers and 1,600 items. able to meet this challenge.
For very large data sets, a scalable recommen- In the future, we expect the retail industry to
dation algorithm must perform the most expensive more broadly apply recommendation algorithms for
calculations offline. As a brief comparison shows, targeted marketing, both online and offline. While
existing methods fall short: e-commerce businesses have the easiest vehicles for
personalization, the technology’s increased conver-
• Traditional collaborative filtering does little or sion rates as compared with traditional broad-scale
no offline computation, and its online compu- approaches will also make it compelling to offline
tation scales with the number of customers and retailers for use in postal mailings, coupons, and
catalog items. The algorithm is impractical on other forms of customer communication.
IEEE INTERNET COMPUTING http://computer.org/internet/ JANUARY • FEBRUARY 2003 79
JANUARY / FEBRUARY 2003
Advertiser / Product Page Number References
ADVERTISER / PRODUCT INDEX
1. J.B. Schafer, J.A. Konstan, and J. Reidl, “E-Commerce Rec-
ommendation Applications,” Data Mining and Knowledge
John Wiley & Sons Inside Back Cover Discovery, Kluwer Academic, 2001, pp. 115-153.
2. P. Resnick et al., “GroupLens: An Open Architecture for
CTIA Wireless Back Cover Collaborative Filtering of Netnews,” Proc. ACM 1994 Conf.
Computer Supported Cooperative Work, ACM Press, 1994,
Advertising Personnel 3. J. Breese, D. Heckerman, and C. Kadie, “Empirical Analy-
sis of Predictive Algorithms for Collaborative Filtering,”
Marion Delaney Sandy Brown Proc. 14th Conf. Uncertainty in Artificial Intelligence, Mor-
IEEE Media, Advertising Director IEEE Computer Society, gan Kaufmann, 1998, pp. 43-52.
Phone:+1 212 419 7766 Business Development Manager
Fax: +1 212 419 7589 Phone:+1 714 821 8380 4. B.M. Sarwarm et al., “Analysis of Recommendation Algo-
Email: firstname.lastname@example.org Fax: +1 714 821 4010 rithms for E-Commerce,” ACM Conf. Electronic Commerce,
Email: email@example.com ACM Press, 2000, pp.158-167.
Marian Anderson Debbie Sims 5. K. Goldberg et al., “Eigentaste: A Constant Time Collabo-
Advertising Coordinator Assistant Advertising Coordinator rative Filtering Algorithm,” Information Retrieval J., vol.
Phone:+1 714 821 8380 Phone:+1 714 821 8380 4, no. 2, July 2001, pp. 133-151.
Fax: +1 714 821 4010 Fax: +1 714 821 4010
Email: firstname.lastname@example.org 6. P.S. Bradley, U.M. Fayyad, and C. Reina, “Scaling Clustering
Algorithms to Large Databases,” Knowledge Discovery and
Data Mining, Kluwer Academic, 1998, pp. 9-15.
Advertising Sales Representatives 7. L. Ungar and D. Foster, “Clustering Methods for Collabo-
rative Filtering,” Proc. Workshop on Recommendation Sys-
Mid Atlantic (product/recruitment) Southeast (product/recruitment)
tems, AAAI Press, 1998.
Dawn Becker C. William Bentz III
Phone: +1 732 772 0160 Email: email@example.com 8. M. Balabanovic and Y. Shoham, “Content-Based Collabora-
Fax: +1 732 772 0161 Gregory Maddock tive Recommendation,” Comm. ACM, Mar. 1997, pp. 66-72.
Email: firstname.lastname@example.org Email: email@example.com 9. G.D. Linden, J.A. Jacobi, and E.A. Benson, Collaborative
Sarah K. Wiley Recommendations Using Item-to-Item Similarity Mappings,
Midwest (product) Email: firstname.lastname@example.org US Patent 6,266,649 (to Amazon.com), Patent and Trade-
David Kovacs Phone: +1 404 256 3800 mark Office, Washington, D.C., 2001.
Phone: +1 847 705 6867 Fax: +1 404 255 7942 10. B.M. Sarwar et al., “Item-Based Collaborative Filtering Rec-
Fax: +1 847 705 6878
Midwest/Southwest recruitment) ommendation Algorithms,” 10th Int’l World Wide Web
Tom Wilcoxen Conference, ACM Press, 2001, pp. 285-295.
New England (product) Phone: +1 847 498 4520
Jody Estabrook Fax: +1 847 498 5911
Greg Linden was cofounder, researcher, and senior manager in
Phone: +1 978 244 0192 Email: email@example.com
Fax: +1 978 244 0103 the Amazon.com Personalization Group, where he designed
New England (recruitment) and developed the recommendation algorithm. He is cur-
Phone: +1 401 738 6237 rently a graduate student in management in the Sloan Pro-
Southwest (product) Fax: +1 401 739 7970 gram at Stanford University’s Graduate School of Business.
Royce House Email: firstname.lastname@example.org
Phone: +1 713 668 1007 His research interests include recommendation systems, per-
Fax: +1 713 668 1176 Connecticut (product) sonalization, data mining, and artificial intelligence. Linden
Email: email@example.com Stan Greenfield received an MS in computer science from the University of
Phone: +1 203 938 2418
Northwest (product) Washington. Contact him at Linden_Greg@gsb.stanford.edu.
Fax: +1 203 938 3211
John Gibbs Email: firstname.lastname@example.org
Phone: +1 415 929 7619 Brent Smith leads the Automated Merchandising team at Ama-
Fax: +1 415 577 5198 Northwest (recruitment)
Email: email@example.com Mary Tonon zon.com. His research interests include data mining, machine
Phone: +1 415 431 5333 learning, and recommendation systems. He received a BS in
Southern CA (product) Fax: +1 415 431 5335 mathematics from the University of California, San Diego,
Marshall Rubin Email: firstname.lastname@example.org
Phone: +1 818 888 2407 and an MS in mathematics from the University of Washing-
Fax: +1 818 888 4907 Southern CA (recruitment) ton, where he did graduate work in differential geometry.
Email: email@example.com Tim Matteson
Phone: +1 310 836 4064 Contact him at firstname.lastname@example.org.
Midwest (product) Fax: +1 310 836 4067
Dave Jones Email: email@example.com Jeremy York leads the Automated Content Selection and Deliv-
Phone: +1 708 442 5633 Japan ery team at Amazon.com. His interests include statistical
Fax: +1 708 442 7620 German Tajiri
Email: firstname.lastname@example.org models for categorical data, recommendation systems, and
Phone: +81 42 501 9551
Will Hamilton Fax: +81 42 501 9552 optimal choice of Web site display components. He
Phone: +1 269 381 2156 Email: email@example.com received a PhD in statistics from the University of Wash-
Fax: +1 269 381 2556
Email: firstname.lastname@example.org Europe (product) ington, where his thesis won the Leonard J. Savage award
Joe DiNardo Hilary Turnbull for best thesis in applied Bayesian econometrics and sta-
Phone: +1 440 248 2456 Phone: +44 131 660 6605 tistics. Contact him at email@example.com.
Fax: +1 440 248 2594 Fax: +44 131 660 6989
Email: firstname.lastname@example.org Email: email@example.com
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