Every week, I test an application using a natural language interface or a natural language technology and I write an article about my experience with this application.
Comparison shopping engines
We compare all the time and anything to decide which product to buy, which holiday destination to choose, which person to recruit or also to find housing.
Before the development of Internet and the mobile technology, we were going to stores for comparing products in the same category according their price, their technical caracteristics, their dimension, their usability and for buying one of them.
Now, it’s possible to do this tasks from home or outdoor with mobile application. The commercial websites have implemented some functionalities (e.g. shopping basket) to facilitate the buying decision process. Because the user interface is intuitive, the user is able to quickly search his product, pay and confirm the order, which arrives within a matter of days. The commercial websites have adapted the shop surroundings to the numeric with solutions such as ontologies (e.g. Ding, Fensel, Klein, Omelayenko & Schulten, 2003) for representing the supermarket shelves, conversational agents (e.g. Anna on Ikea website, Nina of Nuance, see also the article of Lester, Branting & Mott, 2004) and search engines (e.g. Inbenta) as a substitutes of the seller.
Some commercial websites have an overabundant offer for the user. So, the challenge of comparison engines is to assist the user in determining which product is better according to his personal use, as a seller would do in store. More than eight out of ten users of commercial websites compare the product by price, 50% the comments of people and 45% the technical caracteristics of the product according a study of Click IQ in 2012.
There are many comparison engines. Some are specialized in an area (e.g. Kayak compares the airline tickets by price, duration, times of departure and return), some engines compare according only one criteria (e.g. price comparison engines like Google Shopping or Twenga). Finally, some engines allow to compare products in many areas and according many criterias like findthebest and Versus.
What Versus brings more?
According Versus, the other comparison engines reckons that they often suffer from what the company describes as “the same confused and uninviting data dump approach”. Versus aims to present results more clearly with a better user interface (screenshot 1) and a NLP technology that summarizes the informations (screenshot 2), translates the differences with specific adverbs (screenshot 3) and explains why the criteria is pro with world knowledge (screenshot 4).
The use of Versus is simple. A user simply searches for two cities, as Paris vs London, using the provided text boxes. Versus compares the two cities based on a list of criteria (weather, demography, number of museums etc.) presented in an easy way to understand. Versus compares absolute datas like numbers or finite answers (yes or no) obtained by closed questions.
What is particularly astute on Versus IO is that you can compare the products that are not like-for-like. How to compare the iPhone 5 with a Canon camera, for example? It is more useful than it may seem at first – many people want to know if it is worth buying a point-and-shoot when the camera in their phone is good enough for many circumstances.
Currently, Versus is able to compare mobile phones, cameras, tablets, headphones, messaging apps, weather apps, graphics cards, pc and gaming headsets, sport watches, cities, monitors, game consoles, e-readers and universities. Versus is already available in 18 languages (e.g. english, french, german, japanese, etc.).
Versus illustrates the fact that the differences between things are better showed with a text than a table as does findthebest (screenshot 5). Maybe it’s why Versus is skyrocketing showing an average growth of 35% per month.
However, the application would be even more effective on mobile:
- Personalized comparison
Versus does not include personalized comparison which is considered by FindTheBest using filters. Currently, Versus assumes a thin device is better without wondering what is the need of user. The comparison is always according to personal criteria. If we compare two smartphone it’s to know which is the cheapest, lightest or fastest. Versus lists the differences without allowing the user to sort them according his personal criteria. An appropriate functionality to the mobile would be to have a single search box and/or a speech recognition technology with a linguistic analysis engine to understand what the user wants to compare (e.g. “What are the mobiles with 4G running Android?”). We have the informations (screenshot 6), why do not use them into a linguistic analysis engine?
It would be even possible to make comparisons on usage based. Everybody has not the appropriate technical vocabulary for comparing products. Rather than comparing incomprehensible technical specifications, the user could simply indicate his personal use (e.g. “I navigate on the web and I watch videos”) and the comparison engine could list some mobiles with 4G, a screen resolution of 240×320, a diagonal measurement of 2,5 inches for the comfort. See Wepingo, an e-commerce engine that identifies the need of user with some questions.
- Comparison of user comments
As we said, 50% of the users compare the comments of people. The comparison of these comments could allow to obtain non available informations like the datas about user experience (e.g. the security of an university, the quality of an after-sales service, etc.). This requires reflection on understanding the altered language containing misspellings, abbreviations, emoticons. A survey about the different types of noise that might affect text mining is in (Subramaniam & al., 2009), while an analysis of how noise phenomena, commonly occurring in blogs, affect an opinion mining application is in (Dey and Haque, 2009). Concerning spelling correction literature, there is many works based on word similarity measures, such as the edit distance (Mitton, 1995), anagram hashing (Reynaert, 2006), and semantic distance based on WordNet (Hirst and Budanitsky, 2005)
- Generation of comparative descriptives
For more comprehension, it would be possible to generate comparative descriptives entirely (see approaches en natural language generation, Reither & Dale 2000) as if they have written by a human. How? what approach to obtain a result as below?
“Part of the reason for the size differences is just how heavily the screen dominates each handset. As with nigh-on all modern smartphones, the screen is very much the focal feature.
The iPhone 5S comes with a 4-inch Retina display; a 1136 x 640 resolution resulting in 326ppi. Opting for a ‘bigger is better’ mantra, the Galaxy S4 comes with a 5-inch Full HD Super AMOLED display meaning a 1920 x 1080 resolution and a massive 441ppi.
This added screen real estate means that it perfect for watching movies, or for playing one of the many games that grace the Google Play Store. Being Super AMOLED as well means that colours come highly saturated, although this can be toned down within the settings menu.
There are many that continue to mock Apple for not (yet at least) building a larger iPhone, but equally there are many that feel larger devices are less suited to making phone calls and also doing things like browsing the web more easily with one hand.
Whilst those looking or serious mobile gaming devices might assume the Galaxy S4 is the better choice, they shouldn’t completely write off the iPhone, as even the smaller screen size doesn’t spoil the fact it’s excellent at graphical reproduction” (Source).
I think the combination of a motor language analysis to understand user needs and a motor for generating comparative adapted to the needs is the key to a powerful comparison engine. Personalization is very important in this case!