Yelp's relevancy

Yelp collects and hosts reviews and ratings for all things local. Over years it has collected zillions of such records. Any one in the internet services industry understands that it is like a diamond mine. However, I see quite a few issues with their search. The site is relatively slow compared to many other sites. The local search relevancy is not as good as Google. Hence, it is often easier to find specific local places in yelp by first starting the search in Google. They recently made a few new features such as detection of interesting phrases etc. These NLP features are good but I believe they have only touched the surface of their potential.

Pandora's and Apple

Pandora's business model is based on advertisements that they throw when someone is listening to their radio service. They have both audio and video advertisements. The video ads are quite ineffective because the person listening to the music may not be watching the display of the device. The audio ads are most of the time quite obtrusive and it seems they dont have any concept of relevance while placing the audio ads. Hence, the technology and business of ad supported model is not without flaws. They also are soon going to be subjected to new competition from Apple. Apple already has about 400 Million or more iTune users. It is simpler for them to integrate the pandora like radio seamlessly with every IOS devices. Apple may prefer a subscription model over ad supported model. They also already has a social service called "ping". Overall, Apple no doubt has a better chance in getting most of the market for the collaborative radio service. Pandora should start thinking about 1. business model 2. technology that can support their business model. In my estimation they may get about 2 years time before the Apple gets into the market fully. Hence, Pandora still have time to innovate. 

Image Quality in Online Shopping

It turns out that the perceptual quality of the product images is an important factor for improving customer engagement in e-commerce sites. I have recently published two papers with some of my current team members on studies of impact of image quality in online shopping  ( WWW 2011, MDMKDD 2012).  In these papers, we have shown how one can use an image quality measure to improve the NDCG  of clicks in online shopping sites. This aspect was also studied by marketing researchers in past since it sounds like a no brainer that images are important for online shopping.
However, I believe it can actually be more important in future than it is today with the advent of new technologies in digital photography, availability of high quality camera with smart phones.  The assessment of image quality is a hard problem. In one of the papers I wrote on this topic ( SPIE 2012), I have discussed the notion of image quality in the context of online shopping. However, a lot more work can be done in this area to use high level visual attributes in the assessment of image quality. 

Social shopping

The core idea of social shopping is that we often buy things based on our friends recommendations. The recommendation can be explicit or implicit. In an explicit recommendation, a friend proactively recommends a product to another friend. In an implicit recommendation, a friend broadcasts his or her recent purchases which may trigger a purchase interest to some of his/her friends. Booz & Co released a study estimating that the dollar value of goods sold through social media will reach $30Bil worldwide ($14Bil in the US) by 2015 up from $5Bil worldwide ($1Bil in the US) in 2011. Business Insider published a report estimating that the flash sales segment (e.g. Gilt, Fab, One Kings Lane, ideeli, Totsy, etc.) of social commerce will reach $6Bil by 2015 up from an estimated $1.5Bil in 2011. However, so far the notion of right way to implement a social shopping infrastructure has not moved far beyond posting and broadcasting one's purchases in Facebook.  

Broad queries in Amazon's product search

In web search sometimes users search with a query which covers a broad set of concepts or categories.  In the context of e-commerce, such queries may cover a broad range of products. Typically, a search engines best strategy to deal with such broad queries can be to provide a diverse set of results optimally covering these various concepts or categories. There are no dearth of academic studies on classification of queries.  I have recently noticed that Amazon's product search is not giving very good results for quite a few popular broad queries.

Here are a few example:

Query: Mac

 Query: Foundation