1. 原來的算法模型中，沒有考慮買了又買的物品，對於鋼材行業，用戶的需求是一致的，經常買了又買，所以增加此功能

 a 模型訓練中增加如下代碼
 
        //buy item for users
        JavaPairRDD<String, String> buyItemForUser = data.getBuyEvents().mapToPair(new PairFunction<UserItemEvent, Tuple2<String, String>, Integer>() {
            @Override
            public Tuple2<Tuple2<String, String>, Integer> call(UserItemEvent buyEvent) throws Exception {
                return  new Tuple2<>(new Tuple2<>(buyEvent.getUser(), buyEvent.getItem()), 1);
            }
        }).mapToPair(new PairFunction<Tuple2<Tuple2<String, String>, Integer>, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(Tuple2<Tuple2<String, String>, Integer> element) throws Exception {
                return new Tuple2<>(element._1()._1()+":::"+element._1()._2(), element._2());
            }
        }).reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer integer, Integer integer2) throws Exception {
                return integer + integer2;
            }
        }).mapToPair(new PairFunction<Tuple2<String, Integer>, String, String>() {
            @Override
            public Tuple2<String, String> call(Tuple2<String, Integer> element) throws Exception {
                String temp[]=element._1().split(":::");
                if(temp.length==2){
                    return new Tuple2<>(temp[0], temp[1]);
                }            
                return new Tuple2<>("", "");              
            }
        });
        
        b 增加預測算法
            private List<ItemScore> buyItemForUser(Model model, Query query){
        logger.info("start to add buy item for the user");
        final JavaRDD<ItemScore> matchedUser = model.getUserBuyItem().filter(new Function<Tuple2<String, String>, Boolean>() {
            @Override
            public Boolean call(Tuple2<String, String> userIndex) throws Exception {
                return userIndex._1().equals(query.getUserEntityId());
            }
        }).map(new Function<Tuple2<String,String>,ItemScore>() {
            @Override
            public ItemScore call(Tuple2<String, String> arg0) throws Exception {
                  return new ItemScore(arg0._2(),10);
            }            
        });
        
        return matchedUser.collect();
        
    }
    
    c topItemsForUser 按照你的業務邏輯出來兩者的排序規則

基於物的相似性，也是我們期望，加入以下代碼

//根據事件查找物品的屬性
    private List<Set<String>> getRecentProductCategory(Query query, Model model) {
        try {
            List<Set<String>> result = new ArrayList<>();

            List<Event> events = LJavaEventStore.findByEntity(
                    ap.getAppName(),
                    "user",
                    query.getUserEntityId(),
                    OptionHelper.<String>none(),
                    OptionHelper.some(ap.getSimilarItemEvents()),
                    OptionHelper.some(OptionHelper.some("item")),
                    OptionHelper.<Option<String>>none(),
                    OptionHelper.<DateTime>none(),
                    OptionHelper.<DateTime>none(),
                    OptionHelper.some(10),
                    true,
                    Duration.apply(10, TimeUnit.SECONDS));

            for (final Event event : events) {
                if (event.targetEntityId().isDefined()) {
                    JavaPairRDD<String, Integer> filtered = model.getItemIndex().filter(new Function<Tuple2<String, Integer>, Boolean>() {
                        @Override
                        public Boolean call(Tuple2<String, Integer> element) throws Exception {
                            return element._1().equals(event.targetEntityId().get());
                        }
                    });

                    final String itemIndex = filtered.first()._1();
                    
                    
                    Item item = model.getItems().get(itemIndex);
                    if(item.getCategories()!=null && item.getCategories().size()>0){
                       result.add(item.getCategories());
                    }
                }
            }

            return result;
        } catch (Exception e) {
            logger.error("Error reading recent events for user " + query.getUserEntityId());
            throw new RuntimeException(e.getMessage(), e);
        }
    }
    
    //相似性比較
    private List<ItemScore> similarItemsByCategory(final List<Set<String>> category, Model model, Query query) {
        
        Map<String, Item> items =model.getItems();
        if(items==null || items.size()==0){
            return null;
        }
        
        if(category==null || category.size()==0){
            return null;
        }
        
        JavaRDD<ItemScore> itemScores = model.getItemIndex().map(new Function<Tuple2<String, Integer>, ItemScore>() {
            @Override
            public ItemScore call(Tuple2<String, Integer> idItem) throws Exception {
                String itemid= idItem._1();
                Item item = items.get(itemid);            
                double similarity = 0.0;
                for(int i=0 ; i<category.size(); i++){
                    similarity+=getDistance(category.get(i),item.getCategories());
                }
                logger.info(itemid+"->"+similarity);
                return (new ItemScore(itemid, similarity));
                
            }
        });
        
        itemScores = validScores(itemScores, query.getWhitelist(), query.getBlacklist(), query.getCategories(), model.getItems(), query.getUserEntityId());
        
        return sortAndTake(itemScores, query.getNumber());
        
      /*  List<ItemScore> itemScores=new ArrayList<ItemScore>();
        
        for (Map.Entry<String, Item> entry : items.entrySet()) {
             Item it = entry.getValue();
             double similarity = 0.0;
             for(int i=0 ; i<category.size(); i++){
                 similarity+=getDistance(category.get(i),it.getCategories());
             }
             itemScores.add(new ItemScore(it.getEntityId(), similarity));
        }
        
        
        
        itemScores = validScoresForList(itemScores, query.getWhitelist(), query.getBlacklist(), query.getCategories(), model.getItems(), query.getUserEntityId());
        return sortAndTake(itemScores, query.getNumber());*/
    }
    
    //相似算法，比較簡單
    public static int getDistance(Set<String> t, Set<String> s) {  
        if (t==null || t.size()==0 || s==null || s.size()==0 || t.size() != s.size()) {            
            return 0;
        }
        HashSet<String> t_temp=new HashSet<String>(t);//必須轉一下
        HashSet<String> s_temp=new HashSet<String>(s);
        t_temp.retainAll(s_temp);
        return t_temp.size();
        
    }
    
    最後按照你的業務邏輯，加入相似的物品。

predictionIO E-Commerce Recommendation 買了又買-物相似